Diagnosis of sepsis

ABSTRACT

Methods for predicting the development of sepsis in a subject at risk for developing sepsis are provided. In one method, features in a biomarker profile of the subject are evaluated. The subject is likely to develop sepsis if these features satisfy a particular value set. Methods for predicting the development of a stage of sepsis in a subject at risk for developing a stage of sepsis are provided. In one method, a plurality of features in a biomarker profile of the subject is evaluated. The subject is likely to have the stage of sepsis if these feature values satisfy a particular value set. Methods of diagnosing sepsis in a subject are provided. In one such method, a plurality of features in a biomarker profile of the subject is evaluated. The subject is likely to develop sepsis when the plurality of features satisfies a particular value set.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.13/965,038, filed Aug. 12, 2013, which is a continuation of U.S. patentapplication Ser. No. 12/776,245, filed May 7, 2010, which is acontinuation of U.S. patent application Ser. No. 11/404,744, filed Apr.14, 2006, now patented as U.S. Pat. No. 7,767,395, which claims thebenefit of U.S. Provisional Patent Application No. 60/671,620, filedApr. 15, 2005, and U.S. Provisional Patent Application No. 60/674,046,filed Apr. 22, 2005, each of which is hereby incorporated by referencein its entirety into this application.

1. FIELD OF THE INVENTION

The present invention relates to methods and compositions for diagnosingor predicting sepsis and/or its stages of progression in a subject. Thepresent invention also relates to methods and compositions fordiagnosing systemic inflammatory response syndrome in a subject.

2. BACKGROUND OF THE INVENTION

Early detection of a disease condition typically allows for a moreeffective therapeutic treatment with a correspondingly more favorableclinical outcome. In many cases, however, early detection of diseasesymptoms is problematic due to the complexity of the disease; hence, adisease may become relatively advanced before diagnosis is possible.Systemic inflammatory conditions represent one such class of diseases.These conditions, particularly sepsis, typically, but not always, resultfrom an interaction between a pathogenic microorganism and the host'sdefense system that triggers an excessive and dysregulated inflammatoryresponse in the host. The complexity of the host's response during thesystemic inflammatory response has complicated efforts towardsunderstanding disease pathogenesis (reviewed in Healy, 2002, Annul.Pharmacother. 36:648-54). An incomplete understanding of the diseasepathogenesis, in turn, contributes to the difficulty in finding usefuldiagnostic biomarkers. Early and reliable diagnosis is imperative,however, because of the remarkably rapid progression of sepsis into alife-threatening condition.

The development of sepsis in a subject follows a well-described course,progressing from systemic inflammatory response syndrome(“SIRS”)-negative, to SIRS-positive, and then to sepsis, which may thenprogress to severe sepsis, septic shock, multiple organ dysfunction(“MOD”), and ultimately death. Sepsis may also arise in an infectedsubject when the subject subsequently develops SIRS. “Sepsis” iscommonly defined as the systemic host response to infection with SIRSplus a documented infection. “Severe sepsis” is associated with MOD,hypotension, disseminated intravascular coagulation (“DIC”) orhypoperfusion abnormalities, including lactic acidosis, oliguria, andchanges in mental status. “Septic shock” is commonly defined assepsis-induced hypotension that is resistant to fluid resuscitation withthe additional presence of hypoperfusion abnormalities.

Documenting the presence of the pathogenic microorganisms that areclinically significant to sepsis has proven difficult. Causativemicroorganisms typically are detected by culturing a subject's blood,sputum, urine, wound secretion, in-dwelling line catheter surfaces, etc.Causative microorganisms, however, may reside only in certain bodymicroenvironments such that the particular material that is cultured maynot contain the contaminating microorganisms. Detection may becomplicated further by low numbers of microorganisms at the site ofinfection. Low numbers of pathogens in blood present a particularproblem for diagnosing sepsis by culturing blood. In one study, forexample, positive culture results were obtained in only 17% of subjectspresenting clinical manifestations of sepsis (Rangel-Frausto et al.,1995, JAMA 273:117-123). Diagnosis can be further complicated bycontamination of samples by non-pathogenic microorganisms. For example,only 12.4% of detected microorganisms were clinically significant in astudy of 707 subjects with septicemia (Weinstein et al., 1997, ClinicalInfectious Diseases 24:584-602).

The difficulty in early diagnosis of sepsis is reflected by the highmorbidity and mortality associated with the disease. Sepsis currently isthe tenth leading cause of death in the United States and is especiallyprevalent among hospitalized patients in non-coronary intensive careunits (ICUs), where it is the most common cause of death. The overallrate of mortality is as high as 35%, with an estimated 750,000 cases peryear occurring in the United States alone. The annual cost to treatsepsis in the United States alone is on the order of billions ofdollars.

A need, therefore, exists for a method of diagnosing sepsis, usingtechniques that have satisfactory specificity and sensitivityperformance, sufficiently early to allow effective intervention andprevention.

3. SUMMARY OF THE INVENTION

The present invention relates to methods and compositions for diagnosingsepsis, including the onset of sepsis, in a test subject. The presentinvention also relates to methods and compositions for predicting sepsisin a test subject.

The present invention further relates to methods and compositions fordiagnosing or predicting stages of sepsis progression in a test subject.The present invention still further relates to methods and compositionsfor diagnosing systemic inflammatory response syndrome (SIRS) in a testsubject.

In one aspect, the present invention provides a method of predicting thedevelopment of sepsis in a test subject at risk for developing sepsis.This method comprises evaluating whether a plurality of features in abiomarker profile of the test subject satisfies a value set, whereinsatisfying the value set means that the test subject will develop sepsiswith a likelihood that is determined by the accuracy of the decisionrule to which the plurality of features are applied in order todetermine whether they satisfy the value set. In some embodiments, theaccuracy of the decision rule is at least 60%. Therefore,correspondingly, the likelihood that the test subject will developsepsis when the plurality of features satisfies the value set is atleast 60%.

Yet another aspect of the invention comprises a method of diagnosingsepsis in a test subject. These methods comprise evaluating whether aplurality of features in a biomarker profile of the test subjectsatisfies a value set, wherein satisfying the value set predicts thatthe test subject has sepsis with a likelihood that is determined by theaccuracy of the decision rule to which the plurality of features areapplied in order to determine whether they satisfy the value set. Insome embodiments, the accuracy of the decision rule is at least 60%.Therefore, correspondingly, the likelihood that the test subject hassepsis when the plurality of features satisfies the value set is atleast 60%.

In a particular embodiment, the biomarker profile comprises at least twofeatures, each feature representing a feature of a correspondingbiomarker listed in column four or five of Table 30. In one embodiment,the biomarker profile comprises at least two different biomarkers listedin column four or five of Table 30. In such an embodiment, the biomarkerprofile can comprise a respective corresponding feature for the at leasttwo biomarkers. Generally, the at least two biomarkers are derived fromat least two different genes. In the case where a biomarker in the atleast two different biomarkers is listed in column four of Table 30, thebiomarker can be, for example, a transcript made by the listed gene, acomplement thereof, or a discriminating fragment or complement thereof,or a cDNA thereof, or a discriminating fragment of the cDNA, or adiscriminating amplified nucleic acid molecule corresponding to all or aportion of the transcript or its complement, or a protein encoded by thegene, or a discriminating fragment of the protein, or an indication ofany of the above. Further still, the biomarker can be, for example, aprotein listed in column five of Table 30, or a discriminating fragmentof the protein, or an indication of any of the above. Here, adiscriminating molecule or fragment is a molecule or fragment that, whendetected, indicates presence or abundance of the above-identifiedtranscript, cDNA, amplified nucleic acid, or protein. In accordance withthis embodiment, the biomarker profiles of the present invention can beobtained using any standard assay known to those skilled in the art, orin an assay described herein, to detect a biomarker. Such assays arecapable, for example, of detecting the products of expression (e.g.,nucleic acids and/or proteins) of a particular gene or allele of a geneof interest (e.g., a gene disclosed in Table 30). In one embodiment,such an assay utilizes a nucleic acid microarray. In some embodiments,the biomarker profile comprises at least two different biomarkers fromcolumn four or five of Table 32. In some embodiments, the biomarkerprofile comprises at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 30, 35, 40, 45, or 50different biomarkers from Table 30.

In a particular embodiment, the biomarker profile comprises at least twodifferent biomarkers that each contain one of the probesets listed incolumn 2 of Table 30, biomarkers that contain the complement of one ofthe probesets of Table 30, or biomarkers that contain an amino acidsequence encoded by a gene that either contains one of the probesets ofTable 30 or the complement of one of the probesets of Table 30. Suchbiomarkers can be, for example, mRNA transcripts, cDNA or some othernucleic acid, for example amplified nucleic acid, or proteins. Thebiomarker profile further comprises a respective corresponding featurefor the at least two biomarkers. Generally, the at least two biomarkersare derived from at least two different genes. In the case where abiomarker is based upon a gene that includes the sequence of a probesetlisted in Table 30, the biomarker can be, for example, a transcript madeby the gene, a complement thereof, or a discriminating fragment orcomplement thereof, or a cDNA thereof, or a discriminating fragment ofthe cDNA, or a discriminating amplified nucleic acid moleculecorresponding to all or a portion of the transcript or its complement,or a protein encoded by the gene, or a discriminating fragment of theprotein, or an indication of any of the above. Further still, thebiomarker can be, for example, a protein encoded by a gene that includesa probeset sequence described in Table 30, or a discriminating fragmentof the protein, or an indication of any of the above. Here, adiscriminating molecule or fragment is a molecule or fragment that, whendetected, indicates presence or abundance of the above-identifiedtranscript, cDNA, amplified nucleic acid, or protein. In someembodiments, the biomarker profile comprises at least 2, 3, 4, 5, 6, 7,8, 9, or 10 different biomarkers from any one of Table 31, 32, 33, 34,or 36.

In a particular embodiment, the biomarker profile comprises at least twodifferent biomarkers listed in column three of Table 31. The biomarkerprofile further comprises a respective corresponding feature for the atleast two biomarkers. Generally, the at least two biomarkers are derivedfrom at least two different genes. The biomarker can be, for example, atranscript made by gene listed in Table 31, a complement thereof, or adiscriminating fragment or complement thereof, or a cDNA thereof, or adiscriminating fragment of the cDNA, or a discriminating amplifiednucleic acid molecule corresponding to all or a portion of thetranscript or its complement, or a protein encoded by the gene, or adiscriminating fragment of the protein, or an indication of any of theabove. Further still, the biomarker can be, for example, a proteinencoded by a gened listed in column three of Table 31, or adiscriminating fragment of the protein, or an indication of any of theabove. Here, a discriminating molecule or fragment is a molecule orfragment that, when detected, indicates presence or abundance of theabove-identified transcript, cDNA, amplified nucleic acid, or protein.In accordance with this embodiment, the biomarker profiles of thepresent invention can be obtained using any standard assay known tothose skilled in the art, or in an assay described herein, to detect abiomarker. Such assays are capable, for example, of detecting theproducts of expression (e.g., nucleic acids and/or proteins) of aparticular gene or allele of a gene of interest (e.g., a gene disclosedin Table 31). In one embodiment, such an assay utilizes a nucleic acidmicroarray.

In a particular embodiment, the biomarker profile comprises at least twodifferent biomarkers that each contain one of the probesets listed incolumn 2 of Table 31, biomarkers that contain the complement of one ofthe probesets of Table 31, or biomarkers that contain an amino acidsequence encoded by a gene that either contains one of the probesets ofTable 31 or the complement of one of the probesets of Table 31. Suchbiomarkers can be, for example, mRNA transcripts, cDNA or some othernucleic acid, for example amplified nucleic acid, or proteins. Thebiomarker profile further comprises a respective corresponding featurefor the at least two biomarkers. Generally, the at least two biomarkersare derived from at least two different genes. In the case where abiomarker is based upon a gene that includes the sequence of a probesetlisted in Table 31, the biomarker can be, for example, a transcript madeby the gene, a complement thereof, or a discriminating fragment orcomplement thereof, or a cDNA thereof, or a discriminating fragment ofthe cDNA, or a discriminating amplified nucleic acid moleculecorresponding to all or a portion of the transcript or its complement,or a protein encoded by the gene, or a discriminating fragment of theprotein, or an indication of any of the above. Further still, thebiomarker can be, for example, a protein encoded by a gene that includesa probeset sequence described in Table 31, or a discriminating fragmentof the protein, or an indication of any of the above. Here, adiscriminating molecule or fragment is a molecule or fragment that, whendetected, indicates presence or abundance of the above-identifiedtranscript, cDNA, amplified nucleic acid, or protein. In someembodiments, the biomarker profile comprises at least 2, 3, 4, 5, 6, 7,8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25,30, 35, 40, 45, or 50 different biomarkers from Table 31.

In a particular embodiment, the biomarker profile comprises at leastthree features, each feature representing a feature of a correspondingbiomarker listed in column 3 or four of Table I. In one embodiment, thebiomarker profile comprises at least three different biomarkers listedin column three or four of Table I. In such an embodiment, the biomarkerprofile can comprise a respective corresponding feature for the at leastthree biomarkers. Generally, the at least three biomarkers are derivedfrom at least three different genes listed in Table I. In the case wherea biomarker in the at least three different biomarkers is listed incolumn three of Table I, the biomarker can be, for example, a transcriptmade by the listed gene, a complement thereof, a splice variant thereof,a complement of a splice variant thereof, or a discriminating fragmentor complement of any of the foregoing, a cDNA of any of the forgoing, adiscriminating fragment of the cDNA, or a discriminating amplifiednucleic acid molecule corresponding to all or a portion of thetranscript or its complement, or a protein encoded by the gene, or adiscriminating fragment of the protein, or an indication of any of theabove. Further still, the biomarker can be, for example, a proteinlisted in column four of Table I, or a discriminating fragment of theprotein, or an indication of any of the above. Here, a discriminatingmolecule or fragment is a molecule or fragment that, when detected,indicates presence or abundance of the above-identified transcript,cDNA, amplified nucleic acid, splice-variant thereof or protein. Inaccordance with this embodiment, the biomarker profiles of the presentinvention can be obtained using any standard assay known to thoseskilled in the art, or in an assay described herein, to detect abiomarker. Such assays are capable, for example, of detecting theproducts of expression (e.g., nucleic acids and/or proteins) of aparticular gene or allele of a gene of interest (e.g., a gene disclosedin Table I). In one embodiment, such an assay utilizes a nucleic acidmicroarray. In some embodiments, the biomarker profile comprises atleast 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,20, 21, 22, 23, 24, 25, 30, 35, 40, 45, or 50 different biomarkers fromTable I.

In a particular embodiment, the biomarker profile comprises at leastthree features, each feature representing a feature of a correspondingbiomarker listed in column 3 or four of Table J. In one embodiment, thebiomarker profile comprises at least three different biomarkers listedin column three or four of Table J. In such an embodiment, the biomarkerprofile can comprise a respective corresponding feature for the at leastthree biomarkers. Generally, the at least three biomarkers are derivedfrom at least three different genes. In the case where a biomarker inthe at least three different biomarkers is listed in column three ofTable J, the biomarker can be, for example, a transcript made by thelisted gene, a complement thereof, a splice variant thereof, acomplement of a splice variant thereof, or a discriminating fragment orcomplement of any of the foregoing, a cDNA of any of the forgoing, adiscriminating fragment of the cDNA, or a discriminating amplifiednucleic acid molecule corresponding to all or a portion of thetranscript or its complement, or a protein encoded by the gene, or adiscriminating fragment of the protein, or an indication of any of theabove. Further still, the biomarker can be, for example, a proteinlisted in column four of Table J, or a discriminating fragment of theprotein, or an indication of any of the above. Here, a discriminatingmolecule or fragment is a molecule or fragment that, when detected,indicates presence or abundance of the above-identified transcript,cDNA, amplified nucleic acid, splice-variant thereof or protein. Inaccordance with this embodiment, the biomarker profiles of the presentinvention can be obtained using any standard assay known to thoseskilled in the art, or in an assay described herein, to detect abiomarker. Such assays are capable, for example, of detecting theproducts of expression (e.g., nucleic acids and/or proteins) of aparticular gene or allele of a gene of interest (e.g., a gene disclosedin Table J). In one embodiment, such an assay utilizes a nucleic acidmicroarray. In some embodiments, the biomarker profile comprises atleast 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,20, 21, 22, 23, 24, 25, 30, 35, 40 different biomarkers from Table J.

In a particular embodiment, the biomarker profile comprises at leastthree features, each feature representing a feature of a correspondingbiomarker listed in column 3 or four of Table K. In one embodiment, thebiomarker profile comprises at least three different biomarkers listedin column three or four of Table K. In such an embodiment, the biomarkerprofile can comprise a respective corresponding feature for the at leastthree biomarkers. Generally, the at least two or three biomarkers arederived from at least two or three different genes, respectively. In thecase where a biomarker in the at least two or three different biomarkersis listed in column three of Table K, the biomarker can be, for example,a transcript made by the listed gene, a complement thereof, a splicevariant thereof, a complement of a splice variant thereof, or adiscriminating fragment or complement of any of the foregoing, a cDNA ofany of the forgoing, a discriminating fragment of the cDNA, or adiscriminating amplified nucleic acid molecule corresponding to all or aportion of the transcript or its complement, or a protein encoded by thegene, or a discriminating fragment of the protein, or an indication ofany of the above. Further still, the biomarker can be, for example, aprotein listed in column four of Table K, or a discriminating fragmentof the protein, or an indication of any of the above. Here, adiscriminating molecule or fragment is a molecule or fragment that, whendetected, indicates presence or abundance of the above-identifiedtranscript, cDNA, amplified nucleic acid, splice-variant thereof orprotein. In accordance with this embodiment, the biomarker profiles ofthe present invention can be obtained using any standard assay known tothose skilled in the art, or in an assay described herein, to detect abiomarker. Such assays are capable, for example, of detecting theproducts of expression (e.g., nucleic acids and/or proteins) of aparticular gene or allele of a gene of interest (e.g., a gene disclosedin Table K). In one embodiment, such an assay utilizes a nucleic acidmicroarray. In some embodiments, the biomarker profile comprises atleast 2, 3, 4, 5, 6, 7, 8, 9, 10 different biomarkers from Table K.

Although the methods of the present invention are particularly usefulfor detecting or predicting the onset of sepsis in SIRS subjects, one ofskill in the art will understand that the present methods may be usedfor any subject: including, but not limited to, subjects suspected ofhaving SIRS or of being at any stage of sepsis. For example, abiological sample can be taken from a subject, and a profile ofbiomarkers in the sample can be evaluated in light of biomarker profilesobtained from several different types of training populations.Representative training populations variously include, for example,populations that include subjects who are SIRS-negative, populationsthat include subjects who are SIRS-positive, and/or populations thatinclude subjects at a particular stage of sepsis. Evaluation of thebiomarker profile in light of each of these different trainingpopulations can be used to determine whether the test subject isSIRS-negative, SIRS-positive, is likely to become septic, or has aparticular stage of sepsis. Based on the diagnosis resulting from themethods of the present invention, an appropriate treatment regimen canthen be initiated.

In particular embodiments, the invention also provides kits that areuseful in diagnosing or predicting the development of sepsis or SIRS ina subject (see Section 5.3, infra). The kits of the present inventioncomprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90,95, 96, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160,165, 170, 175, 180, 185, 190, 195 or 200 or more biomarkers and/orreagents used to detect the presence or abundance of such biomarkers. Insome embodiments, each of these biomarkers is from Table 30. In someembodiments, each of these biomarkers is from Table 31. In someembodiments, each of these biomarkers is from Table 32. In someembodiments, each of these biomarkers is from Table 33. In someembodiments, each of these biomarkers is from Table 36. In someembodiments, each of these biomarkers is from FIG. 39, FIG. 43, FIG. 52,FIG. 53, or FIG. 56. In another embodiment, the kits of the presentinvention comprise at least two, but as many as several hundred or morebiomarkers and/or reagents used to detect the presence or abundance ofsuch biomarkers.

In a specific embodiment, the kits of the present invention comprise atleast 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 96, 100,105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170,175, 180, 185, 190, 195 or 200 or more reagents that specifically bindthe biomarkers of the present invention. For example, such kits cancomprise nucleic acid molecules and/or antibody molecules thatspecifically bind to biomarkers of the present invention.

Specific exemplary biomarkers that are useful in the present inventionare set forth in Section 5.6, Section 5.11, as well as Tables 30, 31,32, 34 and 36 of Section 6. The biomarkers of the kit can be used togenerate biomarker profiles according to the present invention. Examplesof types of biomarkers and/or reagents within such kits include, but arenot limited to, proteins and fragments thereof, peptides, polypeptides,antibodies, proteoglycans, glycoproteins, lipoproteins, carbohydrates,lipids, nucleic acids (mRNA, DNA, cDNA), organic and inorganicchemicals, and natural and synthetic polymers or a discriminatingmolecule or fragment thereof.

In particular embodiments, the invention also provides still other kitsthat are useful in diagnosing or predicting the development of sepsis orSIRS in a subject (see Section 5.3, infra). The kits of the presentinvention comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50 or more biomarkers. Insome embodiments, each of these biomarkers is from Table I. In someembodiments, each of these biomarkers is from Table J. In someembodiments, each of these biomarkers is from Table K. In someembodiments, each of these biomarkers is found in Table I or Table 30.In some embodiments, each of these biomarkers is found in Table I orTable 31. In some embodiments, each of these biomarkers is from FIG. 39,FIG. 43, FIG. 52, FIG. 53, or FIG. 56. In another embodiment, the kitsof the present invention comprise at least two, but as many as 50 ormore biomarkers. In a specific embodiment, the kits of the presentinvention comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80,85, 90, 95, 96, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150,155, 160, 165, 170, 175, 180, 185, 190, 195 or 200 or more reagents thatspecifically bind the biomarkers of the present invention. Specificbiomarkers that are useful in the present invention are set forth inSection 5.6, Section 5.11, as well as Tables I, J, K, L, M, N, and O.The biomarkers of the kits can be used to generate biomarker profilesaccording to the present invention. Examples of classes of compounds ofthe kits include, but are not limited to, proteins and fragmentsthereof, peptides, polypeptides, proteoglycans, glycoproteins,lipoproteins, carbohydrates, lipids, nucleic acids (mRNA, DNA, cDNA),organic and inorganic chemicals, and natural and synthetic polymers or adiscriminating molecule or fragment thereof.

Still another aspect of the present invention comprises computers andcomputer readable media for evaluating whether a test subject is likelyto develop sepsis or SIRS. For instance, one embodiment of the presentinvention provides a computer program product for use in conjunctionwith a computer system. The computer program product comprises acomputer readable storage medium and a computer program mechanismembedded therein. The computer program mechanism comprises instructionsfor evaluating whether a plurality of features in a biomarker profile ofa test subject at risk for developing sepsis satisfies a first valueset. Satisfaction of the first value set predicts that the test subjectis likely to develop sepsis. The features are measurable aspects of aplurality of biomarkers comprising at least three biomarkers listed inTable I. In some embodiments, the computer program product furthercomprises instructions for evaluating whether the plurality of featuresin the biomarker profile of the test subject satisfies a second valueset. Satisfaction of the second value set predicts that the test subjectis not likely to develop sepsis. In some embodiments, the biomarkerprofile has between 3 and 50 biomarkers listed in Table I, between 3 and40 biomarkers listed in Table I, at least four biomarkers listed inTable I, or at least six biomarkers listed in Table I.

Another computer embodiment of the present invention comprises a centralprocessing unit and a memory coupled to the central processing unit. Thememory stores instructions for evaluating whether a plurality offeatures in a biomarker profile of a test subject at risk for developingsepsis satisfies a first value set. Satisfaction of the first value setpredicts that the test subject is likely to develop sepsis. The featuresare measurable aspects of a plurality of biomarkers. This plurality ofbiomarkers comprises at least three biomarkers from Table I. In someembodiments, the memory further stores instructions for evaluatingwhether the plurality of features in the biomarker profile of the testsubject satisfies a second value set, wherein satisfying the secondvalue set predicts that the test subject is not likely to developsepsis. In some embodiments, the biomarker profile consists of between 3and 50 biomarkers listed in Table I, between 3 and 40 biomarkers listedin Table I, at least four biomarkers listed in Table I, or at leasteight biomarkers listed in Table I.

Another computer embodiment in accordance with the present inventioncomprises a computer system for determining whether a subject is likelyto develop sepsis. The computer system comprises a central processingunit and a memory, coupled to the central processing unit. The memorystores instructions for obtaining a biomarker profile of a test subject.The biomarker profile comprises a plurality of features. The pluralityof biomarkers comprises at least three biomarkers listed in Table I. Thememory further comprises instructions for transmitting the biomarkerprofile to a remote computer. The remote computer includes instructionsfor evaluating whether the plurality of features in the biomarkerprofile of the test subject satisfies a first value set. Satisfaction ofthe first value set predicts that the test subject is likely to developsepsis. The memory further comprises instructions for receiving adetermination, from the remote computer, as to whether the plurality offeatures in the biomarker profile of the test subject satisfies thefirst value set. The memory also comprises instructions for reportingwhether the plurality of features in the biomarker profile of the testsubject satisfies the first value set. In some embodiments, the remotecomputer further comprises instructions for evaluating whether theplurality of features in the biomarker profile of the test subjectsatisfies a second value set. Satisfaction of the second value setpredicts that the test subject is not likely to develop sepsis. In suchembodiments, the memory further comprises instructions for receiving adetermination, from the remote computer, as to whether the plurality offeatures in the biomarker profile of the test subject satisfies thesecond set as well as instructions for reporting whether the pluralityof features in the biomarker profile of the test subject satisfies thesecond value set. In some embodiments, the plurality of biomarkerscomprises at least four biomarkers listed in Table I. In someembodiments, the plurality of biomarkers comprises at least sixbiomarkers listed in Table I.

Still another embodiment of the present invention comprises a digitalsignal embodied on a carrier wave comprising a respective value for eachof a plurality of features in a biomarker profile. The features aremeasurable aspects of a plurality of biomarkers. The plurality ofbiomarkers comprises at least three biomarkers listed in Table I. Insome embodiments, the plurality of biomarkers comprises at least fourbiomarkers listed in Table I. In some embodiments, the plurality ofbiomarkers comprises at least eight biomarkers listed in Table I.

Still another aspect of the present invention provides a digital signalembodied on a carrier wave comprising a determination as to whether aplurality of features in a biomarker profile of a test subject satisfiesa value set. The features are measurable aspects of a plurality ofbiomarkers. This plurality of biomarkers comprises at least threebiomarkers listed in Table I. Satisfying the value set predicts that thetest subject is likely to develop sepsis. In some embodiments, theplurality of biomarkers comprises at least four biomarkers listed inTable I. In some embodiments, the plurality of biomarkers comprises atleast eight biomarkers listed in Table I.

Still another embodiment provides a digital signal embodied on a carrierwave comprising a determination as to whether a plurality of features ina biomarker profile of a test subject satisfies a value set. Thefeatures are measurable aspects of a plurality of biomarkers. Theplurality of biomarkers comprises at least three biomarkers listed inTable I. Satisfaction of the value set predicts that the test subject isnot likely to develop sepsis. In some embodiments, the plurality ofbiomarkers comprises at least four biomarkers listed in Table I. In someembodiments, the plurality of biomarkers comprises at least eightbiomarkers listed in Table I.

Still another embodiment of the present invention provides a graphicaluser interface for determining whether a subject is likely to developsepsis. The graphical user interface comprises a display field for adisplaying a result encoded in a digital signal embodied on a carrierwave received from a remote computer. The features are measurableaspects of a plurality of biomarkers. The plurality of biomarkerscomprises at least three biomarkers listed in Table I. The result has afirst value when a plurality of features in a biomarker profile of atest subject satisfies a first value set. The result has a second valuewhen a plurality of features in a biomarker profile of a test subjectsatisfies a second value set. In some embodiments, the plurality ofbiomarkers comprises at least four biomarkers listed in Table I. In someembodiments, the plurality of biomarkers comprises at least eightbiomarkers listed in Table I.

Yet another aspect of the present invention provides a computer systemfor determining whether a subject is likely to develop sepsis. Thecomputer system comprises a central processing unit and a memory,coupled to the central processing unit. The memory stores instructionsfor obtaining a biomarker profile of a test subject. The biomarkerprofile comprises a plurality of features. The features are measurableaspects of a plurality of biomarkers. The plurality of biomarkerscomprise at least three biomarkers listed in Table I. The memory furtherstores instructions for evaluating whether the plurality of features inthe biomarker profile of the test subject satisfies a first value set.Satisfying the first value set predicts that the test subject is likelyto develop sepsis. The memory also stores instructions for reportingwhether the plurality of features in the biomarker profile of the testsubject satisfies the first value set. In some embodiments, theplurality of biomarkers comprises at least four biomarkers listed inTable I. In some embodiments, the plurality of biomarkers comprises atleast eight biomarkers listed in Table I.

4. BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 illustrates a classification and regression tree fordiscriminating between a SIRS phenotypic state characterized by theonset of sepsis and a SIRS phenotypic state characterized by the absenceof sepsis using T⁻³⁶ static data obtained from a training population inaccordance with an embodiment of the present invention.

FIG. 2 shows the distribution of feature values for five biomarkers usedin the decision tree of FIG. 1 across T⁻³⁶ static data obtained from atraining population in accordance with an embodiment of the presentinvention. The biomarkers are referenced by their correspondingAffymetrix U133 plus 2.0 probeset names given in Table 30.

FIG. 3 illustrates the overall accuracy, sensitivity, and specificity of500 trees used to train a decision tree using the Random Forests methodbased upon T⁻³⁶ static data obtained from a training population inaccordance with an embodiment of the present invention.

FIG. 4 illustrates the biomarker importance in the decision rule trainedusing the trees of FIG. 3.

FIG. 5 illustrates the overall accuracy, with 95% confidence intervalbars, specificity, and sensitivity of a decision rule developed withpredictive analysis of microarrays (PAM) using the biomarkers of thepresent invention across T⁻³⁶ static data obtained from a trainingpopulation.

FIG. 6 is a list of biomarkers, rank-ordered by their respective degreesof discriminatory power, identified by PAM using T⁻³⁶ static dataobtained from a training population. The biomarkers are referenced bytheir corresponding Affymetrix U133 plus 2.0 probeset names given inTable 30.

FIG. 7 illustrates CART, PAM, and random forests classificationalgorithm performance data, and associated 95% confidence intervals, forT⁻³⁶ static data obtained from a training population.

FIG. 8 illustrates the number of times that common biomarkers were foundto be important across the decision rules developed using (i) CART, (ii)PAM, (iii) random forests, and (iv) the Wilcoxon (adjusted) test, forT⁻³⁶ static data obtained from a training population.

FIG. 9 illustrates an overall ranking of biomarkers for T⁻³⁶ static dataobtained from a training population. The biomarkers are referenced bytheir corresponding Affymetrix U133 plus 2.0 probeset names given inTable 30.

FIG. 10 illustrates a classification and regression tree fordiscriminating between a SIRS phenotypic state characterized by theonset of sepsis and a SIRS phenotypic state characterized by the absenceof sepsis using data using T⁻¹² static data obtained from a trainingpopulation in accordance with an embodiment of the present invention.

FIG. 11 shows the distribution of feature values for four biomarkersused in the decision tree of FIG. 10 using T⁻¹² static data obtainedfrom a training population in accordance with an embodiment of thepresent invention. The biomarkers are referenced by their correspondingAffymetrix U133 plus 2.0 probeset names given in Table 30.

FIG. 12 illustrates the overall accuracy, sensitivity, and specificityof 500 trees used to train a decision tree using the Random Forestsmethod based upon T⁻¹² static data obtained from a training populationin accordance with an embodiment of the present invention.

FIG. 13 illustrates the biomarker importance in the decision ruletrained using the trees of FIG. 12. The biomarkers are referenced bytheir corresponding Affymetrix U133 plus 2.0 probeset names given inTable 30.

FIG. 14 illustrates a calculation of biomarker importance, summing to100%, determined by a multiple additive regression tree (MART) approachusing T⁻¹² static data obtained from a training population. Thebiomarkers are referenced by their corresponding Affymetrix U133 plus2.0 probeset names given in Table 30.

FIG. 15 illustrates the distribution of feature values of the biomarkersselected by the MART approach illustrated in FIG. 14 between the Sepsisand SIRS groups using T⁻¹² static data obtained from a trainingpopulation. The biomarkers are referenced by their correspondingAffymetrix U133 plus 2.0 probeset names given in Table 30.

FIG. 16 illustrates the overall accuracy, with 95% confidence intervalbars, specificity, and sensitivity of a decision rule developed withpredictive analysis of microarrays (PAM) using the biomarkers of thepresent invention using T⁻¹² static data obtained from a trainingpopulation.

FIG. 17 is a list of biomarkers, rank-ordered by their respectivedegrees of discriminatory power, identified by PAM using T⁻¹² staticdata obtained from a training population. The biomarkers are referencedby their corresponding Affymetrix U133 plus 2.0 probeset names given inTable 30.

FIG. 18 provides a summary of the CART, MART, PAM, and random forests(RF) classification algorithm (decision rule) performance and associated95% confidence intervals using T⁻¹² static data obtained from a trainingpopulation.

FIG. 19 illustrates the number of times that common biomarkers werefound to be important across the decision rules developed using (i)CART, (ii) MART, (iii) PAM, (iv) random forests, and (v) the Wilcoxon(adjusted) test using T⁻¹² static data obtained from a trainingpopulation. The biomarkers are referenced by their correspondingAffymetrix U133 plus 2.0 probeset names given in Table 30.

FIG. 20 illustrates an overall ranking of biomarkers using T⁻¹² staticdata obtained from a training population.

FIG. 21 illustrates a classification and regression tree fordiscriminating between a SIRS phenotypic state characterized by theonset of sepsis and a SIRS phenotypic state characterized by the absenceof sepsis using T⁻¹² baseline data obtained from a training populationin accordance with an embodiment of the present invention.

FIG. 22 shows the distribution of the feature values of five biomarkersused in the decision tree of FIG. 21 using T⁻¹² baseline data obtainedfrom a training population in accordance with an embodiment of thepresent invention. The biomarkers are referenced by their correspondingAffymetrix U133 plus 2.0 probeset names given in Table 30.

FIG. 23 illustrates the overall accuracy, sensitivity, and specificityof 500 trees used to train a decision tree using the Random Forestsmethod using T⁻¹² baseline data obtained from a training population inaccordance with an embodiment of the present invention.

FIG. 24 illustrates the biomarker importance in the decision ruletrained using the trees of FIG. 23. The biomarkers are referenced bytheir corresponding Affymetrix U133 plus 2.0 probeset names given inTable 30.

FIG. 25 illustrates the overall accuracy, with 95% confidence intervalbars, specificity, and sensitivity of a decision rule developed withpredictive analysis of microarrays (PAM) using select biomarkers of thepresent invention and T⁻¹² baseline data obtained from a trainingpopulation.

FIG. 26 is a list of biomarkers, rank-ordered by their respectivedegrees of discriminatory power, identified by PAM using T⁻¹² baselinedata obtained from a training population. The biomarkers are referencedby their corresponding Affymetrix U133 plus 2.0 probeset names given inTable 30.

FIG. 27 illustrates CART, PAM, and random forests classificationalgorithm (decision rule) performance data, and associated 95%confidence intervals, using T⁻¹² baseline data obtained from a trainingpopulation in accordance with an embodiment of the present invention.

FIG. 28 illustrates the number of times that common biomarkers werefound to be important across the decision rules developed using (i)CART, (ii) PAM, (iii) random forests, and (iv) the Wilcoxon (adjusted)test using T⁻¹² baseline data obtained from a training population.

FIG. 29 illustrates an overall ranking of biomarkers for data obtainedusing T⁻¹² baseline data obtained from a training population. Thebiomarkers are referenced by their corresponding Affymetrix U133 plus2.0 probeset names given in Table 30.

FIG. 30 illustrates the filters applied to identify biomarkers thatdiscriminate between subjects that will get sepsis during a defined timeperiod and subjects that will not get sepsis during the defined timeperiod using data obtained from a training population, in accordancewith an embodiment of the present invention. Other combinations ofbiomarkers are disclosed herein including, for example, in Section 5.3and in Section 6.

FIG. 31 shows the correlation between IL18R1 expression, as determinedby RT-PCR, and the intensity of the X206618_at probeset, as determinedusing Affymetrix U133 plus 2.0 microarray measurements, across atraining population.

FIG. 32 shows the correlation between FCGR1A expression, as determinedby RT-PCR, and the intensity of the X214511_x_at, X216950_s_at andX216951_at probesets, as determined using Affymetrix U133 plus 2.0microarray measurements, across a training population.

FIG. 33 shows the correlation between MMP9 expression, as determined byRT-PCR, and the intensity of the X203936_s_at probeset, as determinedusing Affymetrix U133 plus 2.0 microarray measurements, across atraining population.

FIG. 34 shows the correlation between CD86 expression, as determined byRT-PCR, and the intensity of the X205685_at, X205686_s_at, andX210895_s_at probesets, as determined using Affymetrix U133 plus 2.0microarray measurements, across a training population.

FIG. 35 shows a computer system in accordance with the presentinvention.

FIG. 36 illustrates a classification and regression tree fordiscriminating between a SIRS phenotypic state characterized by theonset of sepsis and a SIRS phenotypic state characterized by the absenceof sepsis using T⁻¹² static data obtained from an RT-PCR discoverytraining population in accordance with an embodiment of the presentinvention.

FIG. 37 shows the distribution of feature values for seven biomarkersused in the decision tree of FIG. 36 across T⁻¹² static data obtainedfrom an RT-PCR discovery training population in accordance with anembodiment of the present invention.

FIG. 38 illustrates the overall accuracy, sensitivity, and specificityof 462 trees used to train a decision tree using the Random Forestsmethod based upon T⁻¹² static data obtained from an RT-PCR discoverytraining population in accordance with an embodiment of the presentinvention.

FIG. 39 illustrates the biomarker importance in the decision ruletrained using the trees of FIG. 38.

FIG. 40 illustrates a calculation of biomarker importance, summing to100%, determined by a multiple additive regression tree (MART) approachusing T⁻¹² static data obtained from an RT-PCR discovery trainingpopulation.

FIG. 41 illustrates the distribution of feature values of the biomarkersselected by the MART approach illustrated in FIG. 40 between the Sepsisand SIRS groups using T⁻¹² static data obtained from an RT-PCR discoverytraining population.

FIG. 42 illustrates the overall accuracy, with 95% confidence intervalbars, specificity, and sensitivity of a decision rule developed withpredictive analysis of microarrays (PAM) using the biomarkers of thepresent invention using T⁻¹² static data obtained from an RT-PCRdiscovery training population.

FIG. 43 is a list of biomarkers, rank-ordered by their respectivedegrees of discriminatory power, identified by PAM using T⁻¹² staticdata obtained from an RT-PCR discovery training population.

FIG. 44 provides a summary of the CART, MART, PAM, and random forests(RF) classification algorithm (decision rule) performance and associated95% confidence intervals using T⁻¹² static data obtained from an RT-PCRdiscovery training population.

FIG. 45 identified fifty selected biomarkers selected based on thedecision rule performance summarized in FIG. 44.

FIG. 46 provides a summary of the CART, MART, PAM, and random forests(RF) classification algorithm (decision rule) performance and associated95% confidence intervals using T⁻¹² static data obtained from anAffymetrix gene chip discovery training population.

FIG. 47 provides a summary of the CART, MART, PAM, and random forests(RF) classification algorithm (decision rule) performance and associated95% confidence intervals using T⁻¹² static data obtained from an RT-PCRconfimatory training population.

FIG. 48 provides a summary of the CART, MART, PAM, and random forests(RF) classification algorithm (decision rule) performance and associated95% confidence intervals using T⁻¹² static data obtained from a combinedpool of a Affymetrix gene chip confirmatory training population and anRT-PCR confirmatory training population.

FIG. 49 illustrates a classification and regression tree fordiscriminating between a SIRS phenotypic state characterized by theonset of sepsis and a SIRS phenotypic state characterized by the absenceof sepsis using T⁻¹² static data obtained from a bead-based proteindiscovery training population in accordance with an embodiment of thepresent invention.

FIGS. 50A, 50B and 50C show the distribution of feature values for tenbiomarkers: (A) MIP1beta, thrombopoietin; (B) ICAM1, IL-10, adiponectin,alpha fetoprotein; and (C) IL-16, IL-6, beta-2 microglobulin, and Creactive protein; used in the decision tree of FIG. 49 across T⁻¹²static data obtained from a bead-based protein discovery trainingpopulation in accordance with an embodiment of the present invention.

FIG. 51 illustrates the overall accuracy, sensitivity, and specificityof 64 trees used to train a decision tree using the Random Forestsmethod based upon T⁻¹² static data obtained from a bead-based proteindiscovery training population in accordance with an embodiment of thepresent invention.

FIG. 52 illustrates the biomarker importance in the decision ruletrained using the trees of FIG. 51.

FIG. 53 illustrates a calculation of biomarker importance, summing to100%, determined by a multiple additive regression tree (MART) approachusing T⁻¹² static data obtained from a bead-based protein discoverytraining population in accordance with an embodiment of the presentinvention.

FIG. 54 illustrates the distribution of feature values of the biomarkersselected by the MART approach illustrated in FIG. 53 between the Sepsisand SIRS groups using T⁻¹² static data obtained from a bead-basedprotein discovery training population in accordance with an embodimentof the present invention.

FIG. 55 illustrates the overall accuracy, with 95% confidence intervalbars, specificity, and sensitivity of a decision rule developed withpredictive analysis of microarrays (PAM) using the biomarkers of thepresent invention using T⁻¹² static data obtained from a bead-basedprotein discovery training population in accordance with an embodimentof the present invention.

FIG. 56 is a list of biomarkers, rank-ordered by their respectivedegrees of discriminatory power, identified by PAM using T⁻¹² staticdata obtained from a bead-based protein discovery training population inaccordance with an embodiment of the present invention.

FIG. 57 provides a summary of the CART, MART, PAM, and random forests(RF) classification algorithm (decision rule) performance and associated95% confidence intervals using T⁻¹² static data obtained from abead-based protein discovery training population in accordance with anembodiment of the present invention.

FIG. 58 illustrates the number of times that common biomarkers werefound to be important across the decision rules developed using (i)CART, (ii) MART, (iii) PAM, (iv) random forests, and (v) the Wilcoxon(adjusted) test using T⁻¹² static data obtained from a bead-basedprotein discovery training population in accordance with an embodimentof the present invention.

FIG. 59 provides a summary of the CART, MART, PAM, and random forests(RF) classification algorithm (decision rule) performance and associated95% confidence intervals using T⁻¹² static data obtained from abead-based protein confirmation training population in accordance withan embodiment of the present invention.

FIGS. 60A and 60B plot the sepsis predicting accuracy of each of 24families of subcombinations from Table J, using T⁻¹² nucleic acid data,in a bar graph fashion, in accordance with an embodiment of the presentinvention: (A) 17-25 nucleotides; (B) 2-16 nucleotides.

FIGS. 61A and 61B plot the sepsis predicting performance (accuracy) ofeach individual subcombination in each of 24 families ofsubcombinations, for a total of 4800 subcombinations from Table J, usingT⁻¹² nucleic acid data, in accordance with an embodiment of the presentinvention: (A) 17-25 nucleotides; (B) 2-16 nucleotides.

FIGS. 62A and 62B plot the sepsis predicting accuracy of each of 8families of subcombinations from Table K, using T⁻¹² protein data, in abar graph fashion, in accordance with an embodiment of the presentinvention: (A) 9-10 proteins; (B) 3-8 proteins.

FIGS. 63A and 63B plot the sepsis predicting performance (accuracy) ofeach individual subcombination in each of 8 families of subcombinations,for a total of 1600 subcombinations from Table K, using T⁻¹² proteindata, in accordance with an embodiment of the present invention: (A)9-10 proteins; (B) 3-8 proteins.

FIGS. 64A and 64B plot the sepsis predicting accuracy of each of 8families of subcombinations from Table K, using T⁻³⁶ protein data, in abar graph fashion, in accordance with an embodiment of the presentinvention: (A) 9-10 proteins; (B) 3-8 proteins.

FIGS. 65A and 65B plot the sepsis predicting performance (accuracy) ofeach individual subcombination in each of 8 families of subcombinations,for a total of 1600 subcombinations from Table K, using T⁻³⁶ proteindata, in accordance with an embodiment of the present invention: (A)9-10 proteins; (B) 3-8 proteins.

FIGS. 66A and 66B plot the sepsis predicting accuracy of each of 23families of subcombinations from Table J, using T⁻³⁶ nucleic acid data,in a bar graph fashion, in accordance with an embodiment of the presentinvention: (A) 18-25 nucleotides; (B) 3-17 nucleotides.

FIGS. 67A and 67B plot the sepsis predicting performance (accuracy) ofeach individual subcombination in each of 23 families ofsubcombinations, for a total of 4600 subcombinations from Table J, usingT⁻³⁶ nucleic acid data, in accordance with an embodiment of the presentinvention: (A) 18-25 nucleotides; (B) 3-17 nucleotides.

FIGS. 68A and 68B plot the sepsis predicting accuracy of each of 23families of subcombinations from Table I, using T⁻¹² combined proteinand nucleic acid data, in a bar graph fashion, in accordance with anembodiment of the present invention: (A) 18-25 gene expression andproteins; (B) 3-17 gene expression and proteins.

FIGS. 69A and 69B plot the sepsis predicting performance (accuracy) ofeach individual subcombination in each of 23 families ofsubcombinations, for a total of 4600 subcombinations from Table I, usingT⁻¹² combined protein and nucleic acid data, in accordance with anembodiment of the present invention: (A) 18-25 gene expression andproteins; (B) 3-17 gene expression and proteins.

FIGS. 70A and 70B plot the sepsis predicting accuracy of each of 23families of subcombinations from Table I, using T⁻³⁶ combined proteinand nucleic acid data, in a bar graph fashion, in accordance with anembodiment of the present invention: (A) 18-25 gene expression andproteins; (B) 3-17 gene expression and proteins.

FIGS. 71A and 71B plot the sepsis predicting performance (accuracy) ofeach individual subcombination in each of 23 families ofsubcombinations, for a total of 4600 subcombinations from Table I, usingT⁻³⁶ combined protein and nucleic acid data, in accordance with anembodiment of the present invention: (A) 18-25 gene expression andproteins; (B) 3-17 gene expression and proteins.

5. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

The present invention allows for the rapid and accurate diagnosis orprediction of sepsis by evaluating biomarker features in biomarkerprofiles. These biomarker profiles can be constructed from one or morebiological samples of subjects at a single time point (“snapshot”), ormultiple such time points, during the course of time the subject is atrisk for developing sepsis. Advantageously, sepsis can be diagnosed orpredicted prior to the onset of conventional clinical sepsis symptoms,thereby allowing for more effective therapeutic intervention.

5.1 Definitions

“Systemic inflammatory response syndrome,” or “SIRS,” refers to aclinical response to a variety of severe clinical insults, as manifestedby two or more of the following conditions within a 24-hour period:

-   -   body temperature greater than 38° C. (100.4° F.) or less than        36° C. (96.8° F.);    -   heart rate (HR) greater than 90 beats/minute;    -   respiratory rate (RR) greater than 20 breaths/minute, or P_(CO2)        less than 32 mmHg, or requiring mechanical ventilation; and    -   white blood cell count (WBC) either greater than 12.0×10⁹/L or        less than 4.0×10⁹/L.

These symptoms of SIRS represent a consensus definition of SIRS that canbe modified or supplanted by other definitions in the future. Thepresent definition is used to clarify current clinical practice and doesnot represent a critical aspect of the invention (see, e.g., AmericanCollege of Chest Physicians/Society of Critical Care Medicine ConsensusConference: Definitions for Sepsis and Organ Failure and Guidelines forthe Use of Innovative Therapies in Sepsis, 1992, Crit. Care. Med. 20,864-874, the entire contents of which are herein incorporated byreference).

A subject with SIRS has a clinical presentation that is classified asSIRS, as defined above, but is not clinically deemed to be septic.Methods for determining which subjects are at risk of developing sepsisare well known to those in the art. Such subjects include, for example,those in an ICU and those who have otherwise suffered from aphysiological trauma, such as a burn, surgery or other insult. Ahallmark of SIRS is the creation of a proinflammatory state that can bemarked by tachycardia, tachypnea or hyperpnea, hypotension,hypoperfusion, oliguria, leukocytosis or leukopenia, pyrexia orhypothermia and the need for volume infusion. SIRS characteristicallydoes not include a documented source of infection (e.g., bacteremia).

“Sepsis” refers to a systemic host response to infection with SIRS plusa documented infection (e.g., a subsequent laboratory confirmation of aclinically significant infection such as a positive culture for anorganism). Thus, sepsis refers to the systemic inflammatory response toa documented infection (see, e.g., American College of Chest PhysiciansSociety of Critical Care Medicine, Chest, 1997, 101:1644-1655, theentire contents of which are herein incorporated by reference). As usedherein, “sepsis” includes all stages of sepsis including, but notlimited to, the onset of sepsis, severe sepsis, septic shock andmultiple organ dysfunction (“MOD”) associated with the end stages ofsepsis.

The “onset of sepsis” refers to an early stage of sepsis, e.g., prior toa stage when conventional clinical manifestations are sufficient tosupport a clinical suspicion of sepsis. Because the methods of thepresent invention are used to detect sepsis prior to a time that sepsiswould be suspected using conventional techniques, the subject's diseasestatus at early sepsis can only be confirmed retrospectively, when themanifestation of sepsis is more clinically obvious. The exact mechanismby which a subject becomes septic is not a critical aspect of theinvention. The methods of the present invention can detect the onset ofsepsis independent of the origin of the infectious process.

“Severe sepsis” refers to sepsis associated with organ dysfunction,hypoperfusion abnormalities, or sepsis-induced hypotension.Hypoperfusion abnormalities include, but are not limited to, lacticacidosis, oliguria, or an acute alteration in mental status.

“Septic shock” refers to sepsis-induced hypotension that is notresponsive to adequate intravenous fluid challenge and withmanifestations of peripheral hypoperfusion.

A “converter” or “converter subject” refers to a SIRS-positive subjectwho progresses to clinical suspicion of sepsis during the period thesubject is monitored, typically during an ICU stay.

A “non-converter” or “non-converter subject” refers to a SIRS-positivesubject who does not progress to clinical suspicion of sepsis during theperiod the subject is monitored, typically during an ICU stay.

A “biomarker” is virtually any detectable compound, such as a protein, apeptide, a proteoglycan, a glycoprotein, a lipoprotein, a carbohydrate,a lipid, a nucleic acid (e.g., DNA, such as cDNA or amplified DNA, orRNA, such as mRNA), an organic or inorganic chemical, a natural orsynthetic polymer, a small molecule (e.g., a metabolite), or adiscriminating molecule or discriminating fragment of any of theforegoing, that is present in or derived from a biological sample.“Derived from” as used in this context refers to a compound that, whendetected, is indicative of a particular molecule being present in thebiological sample. For example, detection of a particular cDNA can beindicative of the presence of a particular RNA transcript in thebiological sample. As another example, detection of or binding to aparticular antibody can be indicative of the presence of a particularantigen (e.g., protein) in the biological sample. Here, a discriminatingmolecule or fragment is a molecule or fragment that, when detected,indicates presence or abundance of an above-identified compound.

A biomarker can, for example, be isolated from the biological sample,directly measured in the biological sample, or detected in or determinedto be in the biological sample. A biomarker can, for example, befunctional, partially functional, or non-functional. In one embodimentof the present invention, a biomarker is isolated and used, for example,to raise a specifically-binding antibody that can facilitate biomarkerdetection in a variety of diagnostic assays. Any immunoassay may use anyantibodies, antibody fragment or derivative thereof capable of bindingthe biomarker molecules (e.g., Fab, F(ab′)₂, Fv, or scFv fragments).Such immunoassays are well-known in the art. In addition, if thebiomarker is a protein or fragment thereof, it can be sequenced and itsencoding gene can be cloned using well-established techniques.

As used herein, the term “a species of a biomarker” refers to anydiscriminating portion or discriminating fragment of a biomarkerdescribed herein, such as a splice variant of a particular genedescribed herein (e.g., a gene listed in Table 30, or Table I, or TableJ, or Table K, infra). Here, a discriminating portion or discriminatingfragment is a portion or fragment of a molecule that, when detected,indicates presence or abundance of the above-identified transcript,cDNA, amplified nucleic acid, or protein.

As used herein, the terms “protein”, “peptide”, and “polypeptide” are,unless otherwise indicated, interchangeable.

A “biomarker profile” comprises a plurality of one or more types ofbiomarkers (e.g., an mRNA molecule, a cDNA molecule, a protein and/or acarbohydrate, etc.), or an indication thereof, together with a feature,such as a measurable aspect (e.g., abundance) of the biomarkers. Abiomarker profile comprises at least two such biomarkers or indicationsthereof, where the biomarkers can be in the same or different classes,such as, for example, a nucleic acid and a carbohydrate. A biomarkerprofile may also comprise at least 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 30, 35, 40, 45, 50, 55,60, 65, 70, 75, 80, 85, 90, 95, or 100 or more biomarkers or indicationsthereof. In one embodiment, a biomarker profile comprises hundreds, oreven thousands, of biomarkers or indications thereof. A biomarkerprofile can further comprise one or more controls or internal standards.In one embodiment, the biomarker profile comprises at least onebiomarker, or indication thereof, that serves as an internal standard.In another embodiment, a biomarker profile comprises an indication ofone or more types of biomarkers. The term “indication” as used herein inthis context merely refers to a situation where the biomarker profilecontains symbols, data, abbreviations or other similar indicia for abiomarker, rather than the biomarker molecular entity itself. Forinstance, consider an exemplary biomarker profile of the presentinvention that comprises the Affymetrix (Santa Clara, Calif.) U133 plus2.0 205013_s_at and 209369_at probesets. Another exemplary biomarkerprofile of the present invention comprises the name of genes used toderive the Affymetrix (Santa Clara, Calif.) U133 plus 2.0 205013_s_atand 209369_at probesets. In still another exemplary biomarker profile ofthe present invention, the biomarker profile comprises a physicalquantity of a transcript of a gene from which the 205013_s_at probesetwas derived, and a physical quantity of a transcript of a gene fromwhich the 209369_at probeset was derived. In another embodiment, thebiomarker profile comprises a nominal indication of the quantity of atranscript of a gene from which the 205013_s_at probeset was derived anda nominal indication of the quantity of transcript of a gene from whichthe 209369_at probeset was derived. Still another exemplary biomarkerprofile of the present invention comprises a microarray to which aphysical quantity of a gene transcript from which the 205013_s_atprobeset was derived is bound at a first probe spot on the microarrayand an abundance of a gene transcript from which the 209369_at probesetwas derived is bound to a second probe spot on the microarray. In thislast exemplary biomarker profile, at least twenty percent, fortypercent, or more than forty percent of the probes spots are based onsequences in the probesets given in Table 30. In another exemplarybiomarker profile, at least twenty percent, forty percent, or more thanforty percent of the probes spots are based on sequences in theprobesets given in Table 31.

Each biomarker in a biomarker profile includes a corresponding“feature.” A “feature”, as used herein, refers to a measurable aspect ofa biomarker. A feature can include, for example, the presence or absenceof biomarkers in the biological sample from the subject as illustratedin exemplary biomarker profile 1:

Exemplary Biomarker Profile 1.

Feature Biomarker Presence in sample transcript of gene A Presenttranscript of gene B Absent

In exemplary biomarker profile 1, the feature value for the transcriptof gene A is “presence” and the feature value for the transcript of geneB is “absence.”

A feature can include, for example, the abundance of a biomarker in thebiological sample from a subject as illustrated in exemplary biomarkerprofile 2:

Exemplary Biomarker Profile 2.

Feature Abundance in sample Biomarker in relative units transcript ofgene A 300 transcript of gene B 400

In exemplary biomarker profile 2, the feature value for the transcriptof gene A is 300 units and the feature value for the transcript of geneB is 400 units.

A feature can also be a ratio of two or more measurable aspects of abiomarker as illustrated in exemplary biomarker profile 3:

Exemplary Biomarker Profile 3.

Feature Ratio of abundance of transcript of Biomarker gene A/transcriptof gene B transcript of gene A 300/400 transcript of gene B

In exemplary biomarker profile 3, the feature value for the transcriptof gene A and the feature value for the transcript of gene B is 0.75(300/400).

A feature may also be the difference between a measurable aspect of thecorresponding biomarker that is taken from two samples, where the twosamples are collected from a subject at two different time points. Forexample, consider the case where the biomarker is a transcript of a geneA and the “measurable aspect” is abundance of the transcript, in samplesobtained from a test subject as determined by, e.g., RT-PCR ormicroarray analysis. In this example, the abundance of the transcript ofgene A is measured in a first sample as well as a second sample. Thefirst sample is taken from the test subject a number of hours before thesecond sample. To compute the feature for gene A, the abundance of thetranscript of gene A in one sample is subtracted from the abundance ofthe transcript of gene A in the second sample. A feature can also be anindication as to whether an abundance of a biomarker is increasing inbiological samples obtained from a subject over time and/or anindication as to whether an abundance of a biomarker is decreasing inbiological samples obtained from a subject over time.

In some embodiments, there is a one-to-one correspondence betweenfeatures and biomarkers in a biomarker profile as illustrated inexemplary biomarker profile 1, above. In some embodiments, therelationship between features and biomarkers in a biomarker profile ofthe present invention is more complex, as illustrated in Exemplarybiomarker profile 3, above.

Those of skill in the art will appreciate that other methods ofcomputation of a feature can be devised and all such methods are withinthe scope of the present invention. For example, a feature can representthe average of an abundance of a biomarker across biological samplescollected from a subject at two or more time points. Furthermore, afeature can be the difference or ratio of the abundance of two or morebiomarkers from a biological sample obtained from a subject in a singletime point. A biomarker profile may also comprise at least three, four,five, 10, 20, 30 or more features. In one embodiment, a biomarkerprofile comprises hundreds, or even thousands, of features.

In some embodiments, features of biomarkers are measured usingmicroarrays. The construction of microarrays and the techniques used toprocess microarrays in order to obtain abundance data is well known, andis described, for example, by Draghici, 2003, Data Analysis Tools forDNA Microarrays, Chapman & Hall/CRC, and international publicationnumber WO 03/061564, each of which is hereby incorporated by referencein its entirety. A microarray comprises a plurality of probes. In someinstances, each probe recognizes, e.g., binds to, a different biomarker.In some instances, two or more different probes on a microarrayrecognize, e.g., bind to, the same biomarker. Thus, typically, therelationship between probe spots on the microarray and a subjectbiomarker is a two to one correspondence, a three to one correspondence,or some other form of correspondence. However, it can be the case thatthere is a unique one-to-one correspondence between probes on amicroarray and biomarkers.

A “phenotypic change” is a detectable change in a parameter associatedwith a given state of the subject. For instance, a phenotypic change caninclude an increase or decrease of a biomarker in a bodily fluid, wherethe change is associated with SIRS, sepsis, the onset of sepsis or witha particular stage in the progression of sepsis. A phenotypic change canfurther include a change in a detectable aspect of a given state of thesubject that is not a change in a measurable aspect of a biomarker. Forexample, a change in phenotype can include a detectable change in bodytemperature, respiration rate, pulse, blood pressure, or otherphysiological parameter. Such changes can be determined via clinicalobservation and measurement using conventional techniques that arewell-known to the skilled artisan.

As used herein, the term “complementary,” in the context of a nucleicacid sequence (e.g., a nucleotide sequence encoding a gene describedherein), refers to the chemical affinity between specific nitrogenousbases as a result of their hydrogen bonding properties. For example,guanine (G) forms a hydrogen bond with only cytosine (C), while adenineforms a hydrogen bond only with thymine (T) in the case of DNA, anduracil (U) in the case of RNA. These reactions are described as basepairing, and the paired bases (G with C, or A with T/U) are said to becomplementary. Thus, two nucleic acid sequences may be complementary iftheir nitrogenous bases are able to form hydrogen bonds. Such sequencesare referred to as “complements” of each other. Such complementsequences can be naturally occurring, or, they can be chemicallysynthesized by any method known to those skilled in the art, as forexample, in the case of antisense nucleic acid molecules which arecomplementary to the sense strand of a DNA molecule or an RNA molecule(e.g., an mRNA transcript). See, e.g., Lewin, 2002, Genes VII. OxfordUniversity Press Inc., New York, N.Y., which is hereby incorporated byreference.

As used herein, “conventional techniques” in the context of diagnosingor predicting sepsis or SIRS are those techniques that classify asubject based on phenotypic changes without obtaining a biomarkerprofile according to the present invention.

A “decision rule” is a method used to evaluate biomarker profiles. Suchdecision rules can take on one or more forms that are known in the art,as exemplified in Hastie et al., 2001, The Elements of StatisticalLearning, Springer-Verlag, New York, which is hereby incorporated byreference in its entirety. A decision rule may be used to act on a dataset of features to, inter alia, predict the onset of sepsis, todetermine the progression of sepsis, or to diagnose sepsis. Exemplarydecision rules that can be used in some embodiments of the presentinvention are described in further detail in Section 5.5, below.

“Predicting the development of sepsis” is the determination as towhether a subject will develop sepsis. Any such prediction is limited bythe accuracy of the means used to make this determination. The presentinvention provides a method, e.g., by utilizing a decision rule(s), formaking this determination with an accuracy that is 60% or greater. Asused herein, the terms “predicting the development of sepsis” and“predicting sepsis” are interchangeable. In some embodiments, the act ofpredicting the development of sepsis (predicting sepsis) is accomplishedby evaluating one or more biomarker profiles from a subject using adecision rule that is indicative of the development of sepsis and, as aresult of this evaluation, receiving a result from the decision rulethat indicates that the subject will become septic. Such an evaluationof one or more biomarker profiles from a test subject using a decisionrule uses some or all the features in the one or more biomarker profilesto obtain such a result.

The terms “obtain” and “obtaining,” as used herein, mean “to come intopossession of,” or “coming into possession of,” respectively. This canbe done, for example, by retrieving data from a data store in a computersystem. This can also be done, for example, by direct measurement.

As used herein, the term “specifically,” and analogous terms, in thecontext of an antibody, refers to peptides, polypeptides, and antibodiesor fragments thereof that specifically bind to an antigen or a fragmentand do not specifically bind to other antigens or other fragments. Apeptide or polypeptide that specifically binds to an antigen may bind toother peptides or polypeptides with lower affinity, as determined bystandard experimental techniques, for example, by any immunoassaywell-known to those skilled in the art. Such immunoassays include, butare not limited to, radioimmunoassays (RIAs) and enzyme-linkedimmunosorbent assays (ELISAs). Antibodies or fragments that specificallybind to an antigen may be cross-reactive with related antigens.Preferably, antibodies or fragments thereof that specifically bind to anantigen do not cross-react with other antigens. See, e.g., Paul, ed.,2003, Fundamental Immunology, 5th ed., Raven Press, New York at pages69-105, which is incorporated by reference herein, for a discussionregarding antigen-antibody interactions, specificity andcross-reactivity, and methods for determining all of the above.

As used herein, a “subject” is an animal, preferably a mammal, morepreferably a non-human primate, and most preferably a human. The terms“subject” “individual” and “patient” are used interchangeably herein.

As used herein, a “test subject,” typically, is any subject that is notin a training population used to construct a decision rule. A testsubject can optionally be suspected of either having sepsis at risk ofdeveloping sepsis.

As used herein, a “tissue type,” is a type of tissue. A tissue is anassociation of cells of a multicellular organism, with a commonembryoloical origin or pathway and similar structure and function.Often, cells of a tissue are contiguous at cell membranes butoccasionally the tissue may be fluid (e.g., blood). Cells of a tissuemay be all of one type (a simple tissue, e.g., squamous epithelium,plant parentchyma) or of more than one type (a mixed tissue, e.g.,connective tissue).

As used herein, a “training population” is a set of samples from apopulation of subjects used to construct a decision rule, using a dataanalysis algorithm, for evaluation of the biomarker profiles of subjectsat risk for developing sepsis. In a preferred embodiment, a trainingpopulation includes samples from subjects that are converters andsubjects that are nonconverters.

As used herein, a “data analysis algorithm” is an algorithm used toconstruct a decision rule using biomarker profiles of subjects in atraining population. Representative data analysis algorithms aredescribed in Section 5.5. A “decision rule” is the final product of adata analysis algorithm, and is characterized by one or more value sets,where each of these value sets is indicative of an aspect of SIRS, theonset of sepsis, sepsis, or a prediction that a subject will acquiresepsis. In one specific example, a value set represents a predictionthat a subject will develop sepsis. In another example, a value setrepresents a prediction that a subject will not develop sepsis.

As used herein, a “validation population” is a set of samples from apopulation of subjects used to determine the accuracy of a decisionrule. In a preferred embodiment, a validation population includessamples from subjects that are converters and subjects that arenonconverters. In a preferred embodiment, a validation population doesnot include subjects that are part of the training population used totrain the decision rule for which an accuracy measurement is sought.

As used herein, a “value set” is a combination of values, or ranges ofvalues for features in a biomarker profile. The nature of this value setand the values therein is dependent upon the type of features present inthe biomarker profile and the data analysis algorithm used to constructthe decision rule that dictates the value set. To illustrate, reconsiderexemplary biomarker profile 2:

Exemplary Biomarker Profile 2.

Feature Abundance in sample Biomarker in relative units transcript ofgene A 300 transcript of gene B 400

In this example, the biomarker profile of each member of a trainingpopulation is obtained. Each such biomarker profile includes a measuredfeature, here abundance, for the transcript of gene A, and a measuredfeature, here abundance, for the transcript of gene B. These featurevalues, here abundance values, are used by a data analysis algorithm toconstruct a decision rule. In this example, the data analysis algorithmis a decision tree, described in Section 5.5.1 and the final product ofthis data analysis algorithm, the decision rule, is a decision tree. Anexemplary decision tree is illustrated in FIG. 1. The decision ruledefines value sets. One such value set is predictive of the onset ofsepsis. A subject whose biomarker feature values satisfy this value setis likely to become septic. An exemplary value set of this class isexemplary value set 1:

Exemplary Value Set 1.

Value set component (Abundance in sample Biomarker in relative units)transcript of gene A <400 transcript of gene B <600

Another such value set is predictive of a septic-free state. A subjectwhose biomarker feature values satisfy this value set is not likely tobecome septic. An exemplary value set of this class is exemplary valueset 2:

Exemplary Value Set 2.

Value set component (Abundance in sample Biomarker in relative units)transcript of gene A >400 transcript of gene B >600

In the case where the data analysis algorithm is a neural networkanalysis and the final product of this neural network analysis is anappropriately weighted neural network, one value set is those ranges ofbiomarker profile feature values that will cause the weighted neuralnetwork to indicate that onset of sepsis is likely. Another value set isthose ranges of biomarker profile feature values that will cause theweighted neural network to indicate that onset of sepsis is not likely.

As used herein, the term “probe spot” in the context of a microarrayrefers to a single stranded DNA molecule (e.g., a single stranded cDNAmolecule or synthetic DNA oligomer), referred to herein as a “probe,”that is used to determine the abundance of a particular nucleic acid ina sample. For example, a probe spot can be used to determine the levelof mRNA in a biological sample (e.g., a collection of cells) from a testsubject. In a specific embodiment, a typical microarray comprisesmultiple probe spots that are placed onto a glass slide (or othersubstrate) in known locations on a grid. The nucleic acid for each probespot is a single stranded contiguous portion of the sequence of a geneor gene of interest (e.g., a 10-mer, 11-mer, 12-mer, 13-mer, 14-mer,15-mer, 16-mer, 17-mer, 18-mer, 19-mer, 20-mer, 21-mer, 22-mer, 23-mer,24-mer, 25-mer or larger) and is a probe for the mRNA encoded by theparticular gene or gene of interest. Each probe spot is characterized bya single nucleic acid sequence, and is hybridized under conditions thatcause it to hybridize only to its complementary DNA strand or mRNAmolecule. As such, there can be many probe spots on a substrate, andeach can represent a unique gene or sequence of interest. In addition,two or more probe spots can represent the same gene sequence. In someembodiments, a labeled nucleic sample is hybridized to a probe spot, andthe amount of labeled nucleic acid specifically hybridized to a probespot can be quantified to determine the levels of that specific nucleicacid (e.g., mRNA transcript of a particular gene) in a particularbiological sample. Probes, probe spots, and microarrays, generally, aredescribed in Draghici, 2003, Data Analysis Tools for DNA Microarrays,Chapman & Hall/CRC, Chapter, 2, which is hereby incorporated byreference in its entirety.

As used herein, the term “annotation data” refers to any type of datathat describes a property of a biomarker. Annotation data includes, butis not limited to, biological pathway membership, enzymatic class (e.g.,phosphodiesterase, kinase, metalloproteinase, etc.), protein domaininformation, enzymatic substrate information, enzymatic reactioninformation, protein interaction data, disease association, cellularlocalization, tissue type localization, and cell type localization.

As used herein, the term “T⁻¹²” refers to the last time blood wasobtained from a subject before the subject is clinically diagnosed withsepsis. Since, in the present invention, blood is collected fromsubjects each 24 hour period, T⁻¹² references the average time periodprior to the onset of sepsis for a pool of patients, with some patientsturning septic prior to the 12 hour mark and some patients turningseptic after the 12 hour mark. However, across a pool of subjects, theaverage time period for this last blood sample is the 12 hour mark,hence the name “T⁻¹².”

5.2 Methods for Screening Subjects

The present invention allows for accurate, rapid prediction and/ordiagnosis of sepsis through detection of two or more features of abiomarker profile of a test individual suspected of or at risk fordeveloping sepsis in each of one or more biological samples from a testsubject. In one embodiment, only a single biological sample taken at asingle point in time from the test subject is needed to construct abiomarker profile that is used to make this prediction or diagnosis ofsepsis. In another embodiment, multiple biological samples taken atdifferent points in time from the test subject are used to construct abiomarker profile that is used to make this prediction or diagnosis ofsepsis.

In specific embodiments of the invention, subjects at risk fordeveloping sepsis or SIRS are screened using the methods of the presentinvention. In accordance with these embodiments, the methods of thepresent invention can be employed to screen, for example, subjectsadmitted to an ICU and/or those who have experienced some sort of trauma(such as, e.g., surgery, vehicular accident, gunshot wound, etc.).

In specific embodiments, a biological sample such as, for example,blood, is taken upon admission. In some embodiments, a biological sampleis blood, plasma, serum, saliva, sputum, urine, cerebral spinal fluid,cells, a cellular extract, a tissue specimen, a tissue biopsy, or astool specimen. In some embodiments a biological sample is whole bloodand this whole blood is used to obtain measurements for a biomarkerprofile. In some embodiments a biological sample is some component ofwhole blood. For example, in some embodiments some portion of themixture of proteins, nucleic acid, and/or other molecules (e.g.,metabolites) within a cellular fraction or within a liquid (e.g., plasmaor serum fraction) of the blood is resolved as a biomarker profile. Thiscan be accomplished by measuring features of the biomarkers in thebiomarker profile. In some embodiments, the biological sample is wholeblood but the biomarker profile is resolved from biomarkers in aspecific cell type that is isolated from the whole blood. In someembodiments, the biological sample is whole blood but the biomarkerprofile is resolved from biomarkers expressed or otherwise found inmonocytes that are isolated from the whole blood. In some embodiments,the biological sample is whole blood but the biomarker profile isresolved from biomarkers expressed or otherwise found in red blood cellsthat are isolated from the whole blood. In some embodiments, thebiological sample is whole blood but the biomarker profile is resolvedfrom biomarkers expressed or otherwise found in platelets that areisolated from the whole blood. In some embodiments, the biologicalsample is whole blood but the biomarker profile is resolved frombiomarkers expressed or otherwise found in neutriphils that are isolatedfrom the whole blood. In some embodiments, the biological sample iswhole blood but the biomarker profile is resolved from biomarkersexpressed or otherwise found in eosinophils that are isolated from thewhole blood. In some embodiments, the biological sample is whole bloodbut the biomarker profile is resolved from biomarkers expressed orotherwise found in basophils that are isolated from the whole blood. Insome embodiments, the biological sample is whole blood but the biomarkerprofile is resolved from biomarkers expressed or otherwise found inlymphocytes that are isolated from the whole blood. In some embodiments,the biological sample is whole blood but the biomarker profile isresolved from biomarkers expressed or otherwise found in monocytes thatare isolated from the whole blood. In some embodiments, the biologicalsample is whole blood but the biomarker profile is resolved from one,two, three, four, five, six, or seven cell types from the group of cellstypes consisting of red blood cells, platelets, neutrophils,eosinophils, basophils, lymphocytes, and monocytes.

A biomarker profile comprises a plurality of one or more types ofbiomarkers (e.g., an mRNA molecule, a cDNA molecule, a protein and/or acarbohydrate, etc.), or an indication thereof, together with features,such as a measurable aspect (e.g., abundance) of the biomarkers. Abiomarker profile can comprise at least two such biomarkers orindications thereof, where the biomarkers can be in the same ordifferent classes, such as, for example, a nucleic acid and acarbohydrate. In some embodiments, a biomarker profile comprises atleast 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 96, 100,105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170,175, 180, 185, 190, 195 or 200 or more biomarkers or indicationsthereof. In one embodiment, a biomarker profile comprises hundreds, oreven thousands, of biomarkers or indications thereof. In someembodiments, a biomarker profile comprises at least 2, 3, 4, 5, 6, 7, 8,9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50,or more biomarkers or indications thereof. In one example, in someembodiments, a biomarker profile comprises at least 2, 3, 4, 5, 6, 7, 8,9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50,or more biomarkers selected from Table I of Section 5.11, or indicationsthereof. In another example, in some embodiments, a biomarker profilecomprises at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,17, 18, 19, 20, 25, 30, 35, 40 or more biomarkers selected from Table Jof Section 5.11, or indications thereof. In another example, in someembodiments, a biomarker profile comprises any 2, 3, 4, 5, 6, 7, 8, 9,or all ten biomarkers in Table K of Section 5.11, or indicationsthereof.

In typical embodiments, each biomarker in the biomarker profile isrepresented by a feature. In other words, there is a correspondencebetween biomarkers and features. In some embodiments, the correspondencebetween biomarkers and features is 1:1, meaning that for each biomarkerthere is a feature. In some embodiments, there is more than one featurefor each biomarker. In some embodiments the number of featurescorresponding to one biomarker in the biomarker profile is differentthan then number of features corresponding to another biomarker in thebiomarker profile. As such, in some embodiments, a biomarker profile caninclude at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95,96, 100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160,165, 170, 175, 180, 185, 190, 195 or 200 or more features, provided thatthere are at least 2, 3, 4, 5, 6, or 7 or more biomarkers in thebiomarker profile. In some embodiments, a biomarker profile can includeat least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,20, 25, 30, 35, 40, 45, 50, or more features. Regardless of embodiment,these features can be determined through the use of any reproduciblemeasurement technique or combination of measurement techniques. Suchtechniques include those that are well known in the art including anytechnique described herein or, for example, any technique disclosed inSection 5.4, infra. Typically, such techniques are used to measurefeature values using a biological sample taken from a subject at asingle point in time or multiple samples taken at multiple points intime. In one embodiment, an exemplary technique to obtain a biomarkerprofile from a sample taken from a subject is a cDNA microarray (see,e.g., Section 5.4.1.2 and Section 6, infra). In another embodiment, anexemplary technique to obtain a biomarker profile from a sample takenfrom a subject is a protein-based assay or other form of protein-basedtechnique such as described in the BD Cytometric Bead Array (CBA) HumanInflammation Kit Instruction Manual (BD Biosciences) or the bead assaydescribed in U.S. Pat. No. 5,981,180, each of which is incorporatedherein by reference in their entirety, and in particular for theirteachings of various methods of assay protein concentrations inbiological samples. In still another embodiment, the biomarker profileis mixed, meaning that it comprises some biomarkers that are nucleicacids, or indications thereof, and some biomarkers that are proteins, orindications thereof. In such embodiments, both protein based and nucleicacid based techniques are used to obtain a biomarker profile from one ormore samples taken from a subject. In other words, the feature valuesfor the features associated with the biomarkers in the biomarker profilethat are nucleic acids are obtained by nucleic acid based measurementtechniques (e.g., a nucleic acid microarray) and the feature values forthe features associated with the biomarkers in the biomarker profilethat are proteins are obtained by protein based measurement techniques.In some embodiments biomarker profiles can be obtained using a kit, suchas a kit described in Section 5.3 below.

In specific embodiments, a subject is screened using the methods andcompositions of the invention as frequently as necessary (e.g., duringtheir stay in the ICU) to diagnose or predict sepsis or SIRS in asubject. In a preferred embodiment, the subject is screened soon afterthey arrive in the ICU. In some embodiments, the subject is screeneddaily after they arrive in the ICU. In some embodiments, the subject isscreened every 1 to 4 hours, 1 to 8 hours, 8 to 12 hours, 12 to 16hours, or 16 to 24 hours after they arrive in the ICU.

5.3 Kits

The invention also provides kits that are useful in diagnosing orpredicting the development of sepsis or diagnosing SIRS in a subject. Insome embodiments, the kits of the present invention comprise at least 2,3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30,35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 96, 100, 105, 110,115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180,185, 190, 195 or 200 or more biomarkers and/or reagents to detect thepresence or abundance of such biomarkers. In other embodiments, the kitsof the present invention comprise at least 2, but as many as severalhundred or more biomarkers. In some embodiments, the kits of the presentinvention comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50 or more biomarkersselected from Table I of Section 5.11. In some embodiments, the kits ofthe present invention comprise at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40 or more biomarkersselected from Table J of Section 5.11. In some embodiments, the kits ofthe present invention comprise at least 2, 3, 4, 5, 6, 7, 8, 9, or all10 of the biomarkers in Table K of Section 5.11. In accordance with thedefinition of biomarkers given in Section 5.1, in some instances, abiomarker is in fact a discriminating molecule of, for example, a gene,mRNA, or protein rather than the gene, mRNA, or protein itself. Thus, abiomarker could be a molecule that indicates the presence or abundanceof a particular gene or protein, or fragment thereof, identified in anyone of Tables I, J, or K of Section 5.11 rather than the actual gene orprotein itself. Such discriminating molecules are sometimes referred toin the art as “reagents.” In some embodiments, the kits of the presentinvention comprise at least 2, but as many as several hundred or morebiomarkers.

The biomarkers of the kits of the present invention can be used togenerate biomarker profiles according to the present invention. Examplesof classes of compounds of the kit include, but are not limited to,proteins and fragments thereof, peptides, proteoglycans, glycoproteins,lipoproteins, carbohydrates, lipids, nucleic acids (e.g., DNA, such ascDNA or amplified DNA, or RNA, such as mRNA), organic or inorganicchemicals, natural or synthetic polymers, small molecules (e.g.,metabolites), or discriminating molecules or discriminating fragments ofany of the foregoing. In a specific embodiment, a biomarker is of aparticular size, (e.g., at least 10, 15, 20, 25, 30, 35, 40, 45, 50, 55,60, 65, 70, 75, 80, 85, 90, 95, 100, 105, 110, 115, 120, 125, 130, 135,140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195, 200, 1000,2000, 3000, 5000, 10k, 20k, 100k Daltons or greater). The biomarker(s)may be part of an array, or the biomarker(s) may be packaged separatelyand/or individually. The kit may also comprise at least one internalstandard to be used in generating the biomarker profiles of the presentinvention. Likewise, the internal standard or standards can be any ofthe classes of compounds described above.

In one embodiment, the invention provides kits comprising probes and/orprimers that may or may not be immobilized at an addressable position ona substrate, such as found, for example, in a microarray. In aparticular embodiment, the invention provides such a microarray.

In a specific embodiment, the invention provides a kit for predictingthe development of sepsis in a test subject that comprises a pluralityof antibodies that specifically bind the protein-based biomarkers listedin any one of Tables 30, 31, 32, 33, 34, 36, I, J, or K. In suchembodiments, the antibodies themselves are biomarkers within the scopeof the present invention. In accordance with this embodiment, the kitmay comprise a set of antibodies or functional fragments or derivativesthereof (e.g., Fab, F(ab′)₂, Fv, or scFv fragments) that specificallybind at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95,100, 105, 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165,170, 175, 180, 185, 190, 195 or 200 or more of the protein-basedbiomarkers set forth in any one of Tables 30, 31, 32, 33, 34, 36, I, J,or K. In accordance with this embodiment, the kit may includeantibodies, fragments or derivatives thereof (e.g., Fab, F(ab′)₂, Fv, orscFv fragments) that are specific for the biomarkers of the presentinvention. In one embodiment, the antibodies may be detectably labeled.

In a specific embodiment, the invention provides a kit for predictingthe development of sepsis in a test subject comprises a plurality ofantibodies that specifically bind a plurality of the protein-basedbiomarkers listed in Table I of Section 5.11. In accordance with thisembodiment, the kit may comprise a set of antibodies or functionalfragments or derivatives thereof (e.g., Fab, F(ab′)₂, Fv, or scFvfragments) that specifically bind at least 2, 3, 4, 5, 6, 7, 8, 9, 10,11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50 or moreof the biomarkers set forth in Table I. In accordance with thisembodiment, the kit may include antibodies, fragments or derivativesthereof (e.g., Fab, F(ab′)₂, Fv, or scFv fragments) that are specificfor the biomarkers of the present invention. In one embodiment, theantibodies may be detectably labeled.

In other embodiments of the invention, a kit may comprise a specificbiomarker binding component, such as an aptamer. If the biomarkerscomprise a nucleic acid, the kit may provide an oligonucleotide probethat is capable of forming a duplex with the biomarker or with acomplementary strand of a biomarker. The oligonucleotide probe may bedetectably labeled. In such embodiments, the probes are themselvesbiomarkers that fall within the scope of the present invention.

The kits of the present invention may also include additionalcompositions, such as buffers, that can be used in constructing thebiomarker profile. Prevention of the action of microorganisms can beensured by the inclusion of various antibacterial and antifungal agents,for example, paraben, chlorobutanol, phenol sorbic acid, and the like.It may also be desirable to include isotonic agents such as sugars,sodium chloride, and the like.

Some kits of the present invention comprise a microarray. In oneembodiment this microarray comprises a plurality of probe spots, whereinat least twenty percent of the probe spots in the plurality of probespots correspond to biomarkers in any one of Tables 30, 31, 32, 33, 34,36, I, J, or K. In some embodiments, at least twenty-five percent, atleast thirty percent, at least thirty-five percent, at least fortypercent, or at least sixty percent, or at least eighty percent of theprobe spots in the plurality of probe spots correspond to biomarkers inany one of Tables 30, 31, 32, 33, 34, 36, I, J, or K. Such probe spotsare biomarkers within the scope of the present invention. In someembodiments, the microarray consists of between about three and aboutone hundred probe spots on a substrate. In some embodiments, themicroarray consists of between about three and about one hundred probespots on a substrate. As used in this context, the term “about” meanswithin five percent of the stated value, within ten percent of thestated value, or within twenty-five percent of the stated value. In someembodiments, such microarrays contain one or more probe spots forinter-microarray calibration or for calibration with other microarrayssuch as reference microarrays using techniques that are known to thoseof skill in the art. In some embodiments such microarrays are nucleicacid microarrays. In some embodiments, such microarrays are proteinmicroarrays.

Some kits of the invention may further comprise a computer programproduct for use in conjunction with a computer system, wherein thecomputer program product comprises a computer readable storage mediumand a computer program mechanism embedded therein. In such kits, thecomputer program mechanism comprises instructions for evaluating whethera plurality of features in a biomarker profile of a test subject at riskfor developing sepsis satisfies a first value set. Satisfying the firstvalue set predicts that the test subject is likely to develop sepsis. Inone embodiment, the plurality of features corresponds to biomarkerslisted in any one of Tables 30, 31, 32, 33, 34, 36, I, J, or K. In somekits, the computer program product further comprises instructions forevaluating whether the plurality of features in the biomarker profile ofthe test subject satisfies a second value set. Satisfying the secondvalue set predicts that the test subject is not likely to developsepsis.

Some kits of the present invention comprise a computer having a centralprocessing unit and a memory coupled to the central processing unit. Thememory stores instructions for evaluating whether a plurality offeatures in a biomarker profile of a test subject at risk for developingsepsis satisfies a first value set. Satisfying the first value setpredicts that the test subject is likely to develop sepsis. In oneembodiment, the plurality of features corresponds to biomarkers listedin any one of Tables 30, 31, 32, 33, 34, 36, I, J, or K.

FIG. 35 details an exemplary system that supports the functionalitydescribed above. The system is preferably a computer system 10 having:

-   -   a central processing unit 22;    -   a main non-volatile storage unit 14, for example, a hard disk        drive, for storing software and data, the storage unit 14        controlled by storage controller 12;    -   a system memory 36, preferably high speed random-access memory        (RAM), for storing system control programs, data, and        application programs, comprising programs and data loaded from        non-volatile storage unit 14; system memory 36 may also include        read-only memory (ROM);    -   a user interface 32, comprising one or more input devices (e.g.,        keyboard 28) and a display 26 or other output device;    -   a network interface card 20 for connecting to any wired or        wireless communication network 34 (e.g., a wide area network        such as the Internet);    -   an internal bus 30 for interconnecting the aforementioned        elements of the system; and    -   a power source 24 to power the aforementioned elements.

Operation of computer 10 is controlled primarily by operating system 40,which is executed by central processing unit 22. Operating system 40 canbe stored in system memory 36. In addition to operating system 40, in atypical implementation, system memory 36 includes:

-   -   file system 42 for controlling access to the various files and        data structures used by the present invention;    -   a training data set 44 for use in construction one or more        decision rules in accordance with the present invention;    -   a data analysis algorithm module 54 for processing training data        and constructing decision rules;    -   one or more decision rules 56;    -   a biomarker profile evaluation module 60 for determining whether        a plurality of features in a biomarker profile of a test subject        satisfies a first value set or a second value set;    -   a test subject biomarker profile 62 comprising biomarkers 64        and, for each such biomarkers, features 66; and    -   a database 68 of select biomarkers of the present invention        (e.g., Table 30 and/or Table I and/or Table J and/or Table K,        and/or Table L and/or Table M and/or Table N and/or Table O        etc.) and/or one or features for each of these select        biomarkers.

Training data set 46 comprises data for a plurality of subjects 46. Foreach subject 46 there is a subject identifier 48 and a plurality ofbiomarkers 50. For each biomarker 50, there is at least one feature 52.Although not shown in FIG. 35, for each feature 52, there is a featurevalue. For each decision rule 56 constructed using data analysisalgorithms, there is at least one decision rule value set 58.

As illustrated in FIG. 35, computer 10 comprises software programmodules and data structures. The data structures stored in computer 10include training data set 44, decision rules 56, test subject biomarkerprofile 62, and biomarker database 68. Each of these data structures cancomprise any form of data storage system including, but not limited to,a flat ASCII or binary file, an Excel spreadsheet, a relational database(SQL), or an on-line analytical processing (OLAP) database (MDX and/orvariants thereof). In some specific embodiments, such data structuresare each in the form of one or more databases that include hierarchicalstructure (e.g., a star schema). In some embodiments, such datastructures are each in the form of databases that do not have explicithierarchy (e.g., dimension tables that are not hierarchically arranged).

In some embodiments, each of the data structures stored or accessible tosystem 10 are single data structures. In other embodiments, such datastructures in fact comprise a plurality of data structures (e.g.,databases, files, archives) that may or may not all be hosted by thesame computer 10. For example, in some embodiments, training data set 44comprises a plurality of Excel spreadsheets that are stored either oncomputer 10 and/or on computers that are addressable by computer 10across wide area network 34. In another example, training data set 44comprises a database that is either stored on computer 10 or isdistributed across one or more computers that are addressable bycomputer 10 across wide area network 34.

It will be appreciated that many of the modules and data structuresillustrated in FIG. 35 can be located on one or more remote computers.For example, some embodiments of the present application are webservice-type implementations. In such embodiments, biomarker profileevaluation module 60 and/or other modules can reside on a clientcomputer that is in communication with computer 10 via network 34. Insome embodiments, for example, biomarker profile evaluation module 60can be an interactive web page.

In some embodiments, training data set 44, decision rules 56, and/orbiomarker database 68 illustrated in FIG. 35 are on a single computer(computer 10) and in other embodiments one or more of such datastructures and modules are hosted by one or more remote computers (notshown). Any arrangement of the data structures and software modulesillustrated in FIG. 35 on one or more computers is within the scope ofthe present invention so long as these data structures and softwaremodules are addressable with respect to each other across network 34 orby other electronic means. Thus, the present invention fully encompassesa broad array of computer systems.

Still another kit of the present invention comprises computers andcomputer readable media for evaluating whether a test subject is likelyto develop sepsis or SIRS. For instance, one embodiment of the presentinvention provides a computer program product for use in conjunctionwith a computer system. The computer program product comprises acomputer readable storage medium and a computer program mechanismembedded therein. The computer program mechanism comprises instructionsfor evaluating whether a plurality of features in a biomarker profile ofa test subject at risk for developing sepsis satisfies a first valueset. Satisfaction of the first value set predicts that the test subjectis likely to develop sepsis. The plurality of features are measurableaspects of a plurality of biomarkers, the plurality of biomarkerscomprising at least three biomarkers listed in Table I. In certainembodiments, the plurality of biomarkers comprises at least sixbiomarkers listed in Table I, wherein the plurality of biomarkerscomprises both IL-6 and IL-8. In some embodiments, the computer programproduct further comprises instructions for evaluating whether theplurality of features in the biomarker profile of the test subjectsatisfies a second value set. Satisfaction of the second value setpredicts that the test subject is not likely to develop sepsis. In someembodiments, the biomarker profile has between 3 and 50 biomarkerslisted in Table I, between 3 and 40 biomarkers listed in Table I, atleast four biomarkers listed in Table I, or at least eight biomarkerslisted in Table I.

Another kit of the present invention comprises a central processing unitand a memory coupled to the central processing unit. The memory storesinstructions for evaluating whether a plurality of features in abiomarker profile of a test subject at risk for developing sepsissatisfies a first value set. Satisfaction of the first value setpredicts that the test subject is likely to develop sepsis. Theplurality of features are measurable aspects of a plurality ofbiomarkers. This plurality of biomarkers comprises at least threebiomarkers from Table I. In some embodiments, the plurality ofbiomarkers comprises at least six biomarkers listed in Table I when theplurality of biomarkers comprises both IL-6 and IL-8. In someembodiments, the memory further stores instructions for evaluatingwhether the plurality of features in the biomarker profile of the testsubject satisfies a second value set, wherein satisfying the secondvalue set predicts that the test subject is not likely to developsepsis. In some embodiments, the biomarker profile consists of between 3and 50 biomarkers listed in Table I, between 3 and 40 biomarkers listedin Table I, at least four biomarkers listed in Table I., or at leasteight biomarkers listed in Table I.

Another kit in accordance with the present invention comprises acomputer system for determining whether a subject is likely to developsepsis. The computer system comprises a central processing unit and amemory, coupled to the central processing unit. The memory storesinstructions for obtaining a biomarker profile of a test subject. Thebiomarker profile comprises a plurality of features. Each feature in theplurality of features is a measurable aspect of a correspondingbiomarker in a plurality of biomarkers. The plurality of biomarkerscomprises at least three biomarkers listed in Table I. The memoryfurther comprises instructions for transmitting the biomarker profile toa remote computer. The remote computer includes instructions forevaluating whether the plurality of features in the biomarker profile ofthe test subject satisfies a first value set. Satisfaction of the firstvalue set predicts that the test subject is likely to develop sepsis.The memory further comprises instructions for receiving a determination,from the remote computer, as to whether the plurality of features in thebiomarker profile of the test subject satisfies the first value set. Thememory also comprises instructions for reporting whether the pluralityof features in the biomarker profile of the test subject satisfies thefirst value set. In some embodiments, the plurality of biomarkerscomprises at least six biomarkers listed in Table I when the pluralityof biomarkers comprises both IL-6 and IL-8. In some embodiments, theremote computer further comprises instructions for evaluating whetherthe plurality of features in the biomarker profile of the test subjectsatisfies a second value set. Satisfaction of the second value setpredicts that the test subject is not likely to develop sepsis. In suchembodiments, the memory further comprises instructions for receiving adetermination, from the remote computer, as to whether the plurality offeatures in the biomarker profile of the test subject satisfies thesecond set as well as instructions for reporting whether the pluralityof features in the biomarker profile of the test subject satisfies thesecond value set. In some embodiments, the plurality of biomarkerscomprises at least four biomarkers listed in Table I. In someembodiments, the plurality of biomarkers comprises at least sixbiomarkers listed in Table I.

Still another aspect of the present invention comprises a digital signalembodied on a carrier wave comprising a respective value for each of aplurality of features in a biomarker profile. The plurality of featuresare measurable aspects of a plurality of biomarkers. The plurality ofbiomarkers comprises at least three biomarkers listed in Table I. Insome embodiments, the plurality of biomarkers comprises at least sixbiomarkers listed in Table I when the plurality of biomarkers comprisesboth IL-6 and IL-8. In some embodiments, the plurality of biomarkerscomprises at least four biomarkers listed in Table I. In someembodiments, the plurality of biomarkers comprises at least eightbiomarkers listed in Table I.

Still another aspect of the present invention provides a digital signalembodied on a carrier wave comprising a determination as to whether aplurality of features in a biomarker profile of a test subject satisfiesa value set. The plurality of features are measurable aspects of aplurality of biomarkers. This plurality of biomarkers comprises at leastthree biomarkers listed in Table I. Satisfying the value set predictsthat the test subject is likely to develop sepsis. In some embodiments,the plurality of biomarkers comprises at least six biomarkers listed inTable I when the plurality of biomarkers comprises both IL-6 and IL-8.In some embodiments, the plurality of biomarkers comprises at least fourbiomarkers listed in Table I. In some embodiments, the plurality ofbiomarkers comprises at least eight biomarkers listed in Table I.

Still another embodiment provides a digital signal embodied on a carrierwave comprising a determination as to whether a plurality of features ina biomarker profile of a test subject satisfies a value set. Theplurality of features are measurable aspects of a plurality ofbiomarkers. The plurality of biomarkers comprise at least threebiomarkers listed in Table I. Satisfaction of the value set predictsthat the test subject is not likely to develop sepsis. In someembodiments, the plurality of biomarkers comprises at least sixbiomarkers listed in Table I when the plurality of biomarkers comprisesboth IL-6 and IL-8. In some embodiments, the plurality of biomarkerscomprises at least four biomarkers listed in Table I. In someembodiments, the plurality of biomarkers comprises at least eightbiomarkers listed in Table I.

Still another embodiment of the present invention provides a graphicaluser interface for determining whether a subject is likely to developsepsis. The graphical user interface comprises a display field for adisplaying a result encoded in a digital signal embodied on a carrierwave received from a remote computer. The plurality of features aremeasurable aspects of a plurality of biomarkers. The plurality ofbiomarkers comprise at least three biomarkers listed in Table I. Theresult has a first value when a plurality of features in a biomarkerprofile of a test subject satisfies a first value set. The result has asecond value when a plurality of features in a biomarker profile of atest subject satisfies a second value set. In some embodiments, theplurality of biomarkers comprises at least six biomarkers listed inTable I when the plurality of biomarkers comprises IL-6 and IL-8. Insome embodiments, the plurality of biomarkers comprises at least fourbiomarkers listed in Table I. In some embodiments, the plurality ofbiomarkers comprises at least eight biomarkers listed in Table I.

Yet another kit of the present invention provides a computer system fordetermining whether a subject is likely to develop sepsis. The computersystem comprises a central processing unit and a memory, coupled to thecentral processing unit. The memory stores instructions for obtaining abiomarker profile of a test subject. The biomarker profile comprises aplurality of features. The plurality of features are measurable aspectsof a plurality of biomarkers. The plurality of biomarkers comprise atleast three biomarkers listed in Table I. The memory further storesinstructions for evaluating whether the plurality of features in thebiomarker profile of the test subject satisfies a first value set.Satisfying the first value set predicts that the test subject is likelyto develop sepsis. The memory also stores instructions for reportingwhether the plurality of features in the biomarker profile of the testsubject satisfies the first value set. In some embodiments, theplurality of biomarkers comprises at least six biomarkers listed inTable I when the plurality of biomarkers comprises both IL-6 and IL-8.In some embodiments, the plurality of biomarkers comprises at least fourbiomarkers listed in Table I. In some embodiments, the plurality ofbiomarkers comprises at least eight biomarkers listed in Table I.

5.4 Generation of Biomarker Profiles

According to one embodiment, the methods of the present inventioncomprise generating a biomarker profile from a biological sample takenfrom a subject. The biological sample may be, for example, whole blood,plasma, serum, red blood cells, platelets, neutrophils, eosinophils,basophils, lymphocytes, monocytes, saliva, sputum, urine, cerebralspinal fluid, cells, a cellular extract, a tissue sample, a tissuebiopsy, a stool sample or any sample that may be obtained from a subjectusing techniques well known to those of skill in the art. In a specificembodiment, a biomarker profile is determined using samples collectedfrom a subject at one or more separate time points. In another specificembodiment, a biomarker profile is generated using samples obtained froma subject at separate time points. In one example, these samples areobtained from the subject either once or, alternatively, on a dailybasis, or more frequently, e.g., every 4, 6, 8 or 12 hours. In aspecific embodiment, a biomarker profile is determined using samplescollected from a single tissue type. In another specific embodiment, abiomarker profile is determined using samples collected from at leasttwo different tissue types.

5.4.1 Methods of Detecting Nucleic Acid Biomarkers

In specific embodiments of the invention, biomarkers in a biomarkerprofile are nucleic acids. Such biomarkers and corresponding features ofthe biomarker profile may be generated, for example, by detecting theexpression product (e.g., a polynucleotide or polypeptide) of one ormore genes described herein (e.g., a gene listed in Table 30, Table I,Table J, or Table K.). In a specific embodiment, the biomarkers andcorresponding features in a biomarker profile are obtained by detectingand/or analyzing one or more nucleic acids expressed from a genedisclosed herein (e.g., a gene listed in Table 30, Table I, Table J, orTable K) using any method well known to those skilled in the artincluding, but by no means limited to, hybridization, microarrayanalysis, RT-PCR, nuclease protection assays and Northern blot analysis.

In certain embodiments, nucleic acids detected and/or analyzed by themethods and compositions of the invention include RNA molecules such as,for example, expressed RNA molecules which include messenger RNA (mRNA)molecules, mRNA spliced variants as well as regulatory RNA, cRNAmolecules (e.g., RNA molecules prepared from cDNA molecules that aretranscribed in vitro) and discriminating fragments thereof. Nucleicacids detected and/or analyzed by the methods and compositions of thepresent invention can also include, for example, DNA molecules such asgenomic DNA molecules, cDNA molecules, and discriminating fragmentsthereof (e.g., oligonucleotides, ESTs, STSs, etc.).

The nucleic acid molecules detected and/or analyzed by the methods andcompositions of the invention may be naturally occurring nucleic acidmolecules such as genomic or extragenomic DNA molecules isolated from asample, or RNA molecules, such as mRNA molecules, present in, isolatedfrom or derived from a biological sample. The sample of nucleic acidsdetected and/or analyzed by the methods and compositions of theinvention comprise, e.g., molecules of DNA, RNA, or copolymers of DNAand RNA. Generally, these nucleic acids correspond to particular genesor alleles of genes, or to particular gene transcripts (e.g., toparticular mRNA sequences expressed in specific cell types or toparticular cDNA sequences derived from such mRNA sequences). The nucleicacids detected and/or analyzed by the methods and compositions of theinvention may correspond to different exons of the same gene, e.g., sothat different splice variants of that gene may be detected and/oranalyzed.

In specific embodiments, the nucleic acids are prepared in vitro fromnucleic acids present in, or isolated or partially isolated frombiological a sample. For example, in one embodiment, RNA is extractedfrom a sample (e.g., total cellular RNA, poly(A)⁺ messenger RNA,fraction thereof) and messenger RNA is purified from the total extractedRNA. Methods for preparing total and poly(A)⁺ RNA are well known in theart, and are described generally, e.g., in Sambrook et al., 2001,Molecular Cloning: A Laboratory Manual. 3^(rd) ed. Cold Spring HarborLaboratory Press (Cold Spring Harbor, N.Y.), which is incorporated byreference herein in its entirety. In one embodiment, RNA is extractedfrom a sample using guanidinium thiocyanate lysis followed by CsClcentrifugation and an oligo dT purification (Chirgwin et al., 1979,Biochemistry 18:5294-5299). In another embodiment, RNA is extracted froma sample using guanidinium thiocyanate lysis followed by purification onRNeasy columns (Qiagen, Valencia, Calif.). cDNA is then synthesized fromthe purified mRNA using, e.g., oligo-dT or random primers. In specificembodiments, the target nucleic acids are cRNA prepared from purifiedmessenger RNA extracted from a sample. As used herein, cRNA is definedhere as RNA complementary to the source RNA. The extracted RNAs areamplified using a process in which doubled-stranded cDNAs aresynthesized from the RNAs using a primer linked to an RNA polymerasepromoter in a direction capable of directing transcription of anti-senseRNA. Anti-sense RNAs or cRNAs are then transcribed from the secondstrand of the double-stranded cDNAs using an RNA polymerase (see, e.g.,U.S. Pat. Nos. 5,891,636, 5,716,785; 5,545,522 and 6,132,997, which arehereby incorporated by reference). Both oligo-dT primers (U.S. Pat. Nos.5,545,522 and 6,132,997, hereby incorporated by reference herein) orrandom primers that contain an RNA polymerase promoter or complementthereof can be used. In some embodiments the target nucleic acids areshort and/or fragmented nucleic acid molecules which are representativeof the original nucleic acid population of the sample.

In one embodiment, nucleic acids of the invention can be detectablylabeled. For example, cDNA can be labeled directly, e.g., withnucleotide analogs, or indirectly, e.g., by making a second, labeledcDNA strand using the first strand as a template. Alternatively, thedouble-stranded cDNA can be transcribed into cRNA and labeled.

In some embodiments the detectable label is a fluorescent label, e.g.,by incorporation of nucleotide analogs. Other labels suitable for use inthe present invention include, but are not limited to, biotin,imminobiotin, antigens, cofactors, dinitrophenol, lipoic acid, olefiniccompounds, detectable polypeptides, electron rich molecules, enzymescapable of generating a detectable signal by action upon a substrate,and radioactive isotopes. Suitable radioactive isotopes include ³²P ³⁵S,¹⁴C, ¹⁵N and ¹²⁵I. Fluorescent molecules suitable for the presentinvention include, but are not limited to, fluorescein and itsderivatives, rhodamine and its derivatives, Texas red,5′carboxy-fluorescein (“FMA”),6-carboxy-4′,5′-dichloro-2′,7′-dimethoxyfluorescein, succinimidyl ester(“JOE”), 6-carboxytetramethylrhodamine (“TAMRA”), 6Ncarboxy-X-rhodamine(“ROX”), HEX, TET, IRD40, and IRD41. Fluorescent molecules that aresuitable for the invention further include, but are not limited to:cyamine dyes, including by not limited to Cy3, Cy3.5 and Cy5; BODIPYdyes including but not limited to BODIPY-FL, BODIPY-TR, BODIPY-TMR,BODIPY-630/650, BODIPY-650/670; and ALEXA dyes, including but notlimited to ALEXA-488, ALEXA-532, ALEXA-546, ALEXA-568, and ALEXA-594; aswell as other fluorescent dyes which will be known to those who areskilled in the art. Electron-rich indicator molecules suitable for thepresent invention include, but are not limited to, ferritin, hemocyanin,and colloidal gold. Alternatively, in some embodiments the targetnucleic acids may be labeled by specifically complexing a first group tothe nucleic acid. A second group, covalently linked to an indicatormolecules and which has an affinity for the first group, can be used toindirectly detect the target nucleic acid. In such an embodiment,compounds suitable for use as a first group include, but are not limitedto, biotin and iminobiotin. Compounds suitable for use as a second groupinclude, but are not limited to, avidin and streptavidin.

5.4.1.1 Nucleic Acid Arrays

In certain embodiments of the invention, nucleic acid arrays areemployed to generate features of biomarkers in a biomarker profile bydetecting the expression of any one or more of the genes describedherein (e.g., a gene listed in Table 30, Table I, Table J or Table K).In one embodiment of the invention, a microarray, such as a cDNAmicroarray, is used to determine feature values of biomarkers in abiomarker profile. The diagnostic use of cDNA arrays is well known inthe art. (See, e.g., Zou et. al., 2002, Oncogene 21:4855-4862; as wellas Draghici, 2003, Data Analysis Tools for DNA Microarrays, Chapman &Hall/CRC, each of which is hereby incorporated by reference herein inits entirety). Exemplary methods for cDNA microarray analysis aredescribed below, and in the examples in Section 6, infra.

In certain embodiments, the feature values for biomarkers in a biomarkerprofile are obtained by hybridizing to the array detectably labelednucleic acids representing or corresponding to the nucleic acidsequences in mRNA transcripts present in a biological sample (e.g.,fluorescently labeled cDNA synthesized from the sample) to a microarraycomprising one or more probe spots.

Nucleic acid arrays, for example, microarrays, can be made in a numberof ways, of which several are described herein below. Preferably, thearrays are reproducible, allowing multiple copies of a given array to beproduced and results from said microarrays compared with each other.Preferably, the arrays are made from materials that are stable underbinding (e.g., nucleic acid hybridization) conditions. Those skilled inthe art will know of suitable supports, substrates or carriers forhybridizing test probes to probe spots on an array, or will be able toascertain the same by use of routine experimentation.

Arrays, for example, microarrays, used can include one or more testprobes. In some embodiments each such test probe comprises a nucleicacid sequence that is complementary to a subsequence of RNA or DNA to bedetected. Each probe typically has a different nucleic acid sequence,and the position of each probe on the solid surface of the array isusually known or can be determined. Arrays useful in accordance with theinvention can include, for example, oligonucleotide microarrays, cDNAbased arrays, SNP arrays, spliced variant arrays and any other arrayable to provide a qualitative, quantitative or semi-quantitativemeasurement of expression of a gene described herein (e.g., a genelisted in Table 30, Table I, Table J or Table K). Some types ofmicroarrays are addressable arrays. More specifically, some microarraysare positionally addressable arrays. In some embodiments, each probe ofthe array is located at a known, predetermined position on the solidsupport so that the identity (e.g., the sequence) of each probe can bedetermined from its position on the array (e.g., on the support orsurface). In some embodiments, the arrays are ordered arrays.Microarrays are generally described in Draghici, 2003, Data AnalysisTools for DNA Microarrays, Chapman & Hall/CRC, which is herebyincorporated herein by reference in its entirety.

In some embodiments of the present invention, an expressed transcript(e.g., a transcript of a gene described herein) is represented in thenucleic acid arrays. In such embodiments, a set of binding sites caninclude probes with different nucleic acids that are complementary todifferent sequence segments of the expressed transcript. Exemplarynucleic acids that fall within this class can be of length of 15 to 200bases, 20 to 100 bases, 25 to 50 bases, 40 to 60 bases or some otherrange of bases. Each probe sequence can also comprise one or more linkersequences in addition to the sequence that is complementary to itstarget sequence. As used herein, a linker sequence is a sequence betweenthe sequence that is complementary to its target sequence and thesurface of support. For example, the nucleic acid arrays of theinvention can comprise one probe specific to each target gene or exon.However, if desired, the nucleic acid arrays can contain at least 2, 5,10, 100, or 1000 or more probes specific to some expressed transcript(e.g., a transcript of a gene described herein, e.g., in Table 30, TableI, Table J, or Table K). For example, the array may contain probes tiledacross the sequence of the longest mRNA isoform of a gene.

It will be appreciated that when cDNA complementary to the RNA of acell, for example, a cell in a biological sample, is made and hybridizedto a microarray under suitable hybridization conditions, the level ofhybridization to the site in the array corresponding to a gene describedherein (e.g., a gene listed in Table 30, Table I, Table J, or Table K)will reflect the prevalence in the cell of mRNA or mRNAs transcribedfrom that gene. Alternatively, in instances where multiple isoforms oralternate splice variants produced by particular genes are to bedistinguished, detectably labeled (e.g., with a fluorophore) cDNAcomplementary to the total cellular mRNA can be hybridized to amicroarray, and the site on the array corresponding to an exon of thegene that is not transcribed or is removed during RNA splicing in thecell will have little or no signal (e.g., fluorescent signal), and asite corresponding to an exon of a gene for which the encoded mRNAexpressing the exon is prevalent will have a relatively strong signal.The relative abundance of different mRNAs produced from the same gene byalternative splicing is then determined by the signal strength patternacross the whole set of exons monitored for the gene.

In one embodiment, hybridization levels at different hybridization timesare measured separately on different, identical microarrays. For eachsuch measurement, at hybridization time when hybridization level ismeasured, the microarray is washed briefly, preferably in roomtemperature in an aqueous solution of high to moderate saltconcentration (e.g., 0.5 to 3 M salt concentration) under conditionswhich retain all bound or hybridized nucleic acids while removing allunbound nucleic acids. The detectable label on the remaining, hybridizednucleic acid molecules on each probe is then measured by a method whichis appropriate to the particular labeling method used. The resultinghybridization levels are then combined to form a hybridization curve. Inanother embodiment, hybridization levels are measured in real time usinga single microarray. In this embodiment, the microarray is allowed tohybridize to the sample without interruption and the microarray isinterrogated at each hybridization time in a non-invasive manner. Instill another embodiment, one can use one array, hybridize for a shorttime, wash and measure the hybridization level, put back to the samesample, hybridize for another period of time, wash and measure again toget the hybridization time curve.

In some embodiments, nucleic acid hybridization and wash conditions arechosen so that the nucleic acid biomarkers to be analyzed specificallybind or specifically hybridize to the complementary nucleic acidsequences of the array, typically to a specific array site, where itscomplementary DNA is located.

Arrays containing double-stranded probe DNA situated thereon can besubjected to denaturing conditions to render the DNA single-strandedprior to contacting with the target nucleic acid molecules. Arrayscontaining single-stranded probe DNA (e.g., syntheticoligodeoxyribonucleic acids) may need to be denatured prior tocontacting with the target nucleic acid molecules, e.g., to removehairpins or dimers which form due to self complementary sequences.

Optimal hybridization conditions will depend on the length (e.g.,oligomer versus polynucleotide greater than 200 bases) and type (e.g.,RNA, or DNA) of probe and target nucleic acids. General parameters forspecific (i.e., stringent) hybridization conditions for nucleic acidsare described in Sambrook et al., (supra), and in Ausubel et al., 1988,Current Protocols in Molecular Biology, Greene Publishing andWiley-Interscience, New York. When the cDNA microarrays of Shena et al.are used, typical hybridization conditions are hybridization in 5×SSCplus 0.2% SDS at 65° C. for four hours, followed by washes at 25° C. inlow stringency wash buffer (1×SSC plus 0.2% SDS), followed by 10 minutesat 25° C. in higher stringency wash buffer (0.1×SSC plus 0.2% SDS)(Shena et al., 1996, Proc. Natl. Acad. Sci. U.S.A. 93:10614). Usefulhybridization conditions are also provided in, e.g., Tijessen, 1993,Hybridization With Nucleic Acid Probes, Elsevier Science Publishers B.V.; Kricka, 1992, Nonisotopic DNA Probe Techniques, Academic Press, SanDiego, Calif.; and Zou et. al., 2002, Oncogene 21:4855-4862; andDraghici, Data Analysis Tools for DNA Microanalysis, 2003, CRC PressLLC, Boca Raton, Fla., pp. 342-343, which are hereby incorporated byreference herein in their entirety.

In a specific embodiment, a microarray can be used to sort out RT-PCRproducts that have been generated by the methods described, for example,below in Section 5.4.1.2.

5.4.1.2 RT-PCR

In certain embodiments, to determine the feature values of biomarkers ina biomarker profile of the invention, the level of expression of one ormore of the genes described herein (e.g., a gene listed in Table 30,Table I, Table J, or Table K) is measured by amplifying RNA from asample using reverse transcription (RT) in combination with thepolymerase chain reaction (PCR). In accordance with this embodiment, thereverse transcription may be quantitative or semi-quantitative. TheRT-PCR methods taught herein may be used in conjunction with themicroarray methods described above, for example, in Section 5.4.1.1. Forexample, a bulk PCR reaction may be performed, the PCR products may beresolved and used as probe spots on a microarray. See also Section 6.10,infra.

Total RNA, or mRNA from a sample is used as a template and a primerspecific to the transcribed portion of the gene(s) is used to initiatereverse transcription. Methods of reverse transcribing RNA into cDNA arewell known and described in Sambrook et al., 2001, supra. Primer designcan be accomplished based on known nucleotide sequences that have beenpublished or available from any publicly available sequence databasesuch as GenBank. For example, primers may be designed for any of thegenes described herein (see, e.g., in Table 30, Table I, Table J, orTable K). Further, primer design may be accomplished by utilizingcommercially available software (e.g., Primer Designer 1.0, ScientificSoftware etc.). The product of the reverse transcription is subsequentlyused as a template for PCR.

PCR provides a method for rapidly amplifying a particular nucleic acidsequence by using multiple cycles of DNA replication catalyzed by athermostable, DNA-dependent DNA polymerase to amplify the targetsequence of interest. PCR requires the presence of a nucleic acid to beamplified, two single-stranded oligonucleotide primers flanking thesequence to be amplified, a DNA polymerase, deoxyribonucleosidetriphosphates, a buffer and salts. The method of PCR is well known inthe art. PCR, is performed, for example, as described in Mullis andFaloona, 1987, Methods Enzymol. 155:335, which is hereby incorporatedherein by reference in its entirety.

PCR can be performed using template DNA or cDNA (at least 1 fg; moreusefully, 1-1000 ng) and at least 25 pmol of oligonucleotide primers. Atypical reaction mixture includes: 2 μl of DNA, 25 pmol ofoligonucleotide primer, 2.5 μl of 10 M PCR buffer 1 (Perkin-Elmer,Foster City, Calif.), 0.4 μl of 1.25 M dNTP, 0.15 μl (or 2.5 units) ofTaq DNA polymerase (Perkin Elmer, Foster City, Calif.) and deionizedwater to a total volume of 25 μl. Mineral oil is overlaid and the PCR isperformed using a programmable thermal cycler.

The length and temperature of each step of a PCR cycle, as well as thenumber of cycles, are adjusted according to the stringency requirementsin effect. Annealing temperature and timing are determined both by theefficiency with which a primer is expected to anneal to a template andthe degree of mismatch that is to be tolerated. The ability to optimizethe stringency of primer annealing conditions is well within theknowledge of one of moderate skill in the art. An annealing temperatureof between 30° C. and 72° C. is used. Initial denaturation of thetemplate molecules normally occurs at between 92° C. and 99° C. for 4minutes, followed by 20-40 cycles consisting of denaturation (94-99° C.for 15 seconds to 1 minute), annealing (temperature determined asdiscussed above; 1-2 minutes), and extension (72° C. for 1 minute). Thefinal extension step is generally carried out for 4 minutes at 72° C.,and may be followed by an indefinite (0-24 hour) step at 4° C.

Quantitative RT-PCR (“QRT-PCR”), which is quantitative in nature, canalso be performed to provide a quantitative measure of gene expressionlevels. In QRT-PCR reverse transcription and PCR can be performed in twosteps, or reverse transcription combined with PCR can be performedconcurrently. One of these techniques, for which there are commerciallyavailable kits such as Taqman (Perkin Elmer, Foster City, Calif.) or asprovided by Applied Biosystems (Foster City, Calif.) is performed with atranscript-specific antisense probe. This probe is specific for the PCRproduct (e.g. a nucleic acid fragment derived from a gene) and isprepared with a quencher and fluorescent reporter probe complexed to the5′ end of the oligonucleotide. Different fluorescent markers areattached to different reporters, allowing for measurement of twoproducts in one reaction. When Taq DNA polymerase is activated, itcleaves off the fluorescent reporters of the probe bound to the templateby virtue of its 5′-to-3′ exonuclease activity. In the absence of thequenchers, the reporters now fluoresce. The color change in thereporters is proportional to the amount of each specific product and ismeasured by a fluorometer; therefore, the amount of each color ismeasured and the PCR product is quantified. The PCR reactions areperformed in 96-well plates so that samples derived from manyindividuals are processed and measured simultaneously. The Taqman systemhas the additional advantage of not requiring gel electrophoresis andallows for quantification when used with a standard curve.

A second technique useful for detecting PCR products quantitatively isto use an intercolating dye such as the commercially availableQuantiTect SYBR Green PCR (Qiagen, Valencia Calif.). RT-PCR is performedusing SYBR green as a fluorescent label which is incorporated into thePCR product during the PCR stage and produces a flourescenseproportional to the amount of PCR product.

Both Taqman and QuantiTect SYBR systems can be used subsequent toreverse transcription of RNA. Reverse transcription can either beperformed in the same reaction mixture as the PCR step (one-stepprotocol) or reverse transcription can be performed first prior toamplification utilizing PCR (two-step protocol).

Additionally, other systems to quantitatively measure mRNA expressionproducts are known including Molecular Beacons® which uses a probehaving a fluorescent molecule and a quencher molecule, the probe capableof forming a hairpin structure such that when in the hairpin form, thefluorescence molecule is quenched, and when hybridized the fluorescenceincreases giving a quantitative measurement of gene expression.

Additional techniques to quantitatively measure RNA expression include,but are not limited to, polymerase chain reaction, ligase chainreaction, Qbeta replicase (see, e.g., International Application No.PCT/US87/00880, which is hereby incorporated by reference), isothermalamplification method (see, e.g., Walker et al., 1992, PNAS 89:382-396,which is hereby incorporated herein by reference), strand displacementamplification (SDA), repair chain reaction, Asymmetric Quantitative PCR(see, e.g., U.S. Publication No. US 2003/30134307A1, herein incorporatedby reference) and the multiplex microsphere bead assay described in Fujaet al., 2004, Journal of Biotechnology 108:193-205, herein incorporatedby reference.

The level of expression of one or more of the genes described herein(e.g., the genes listed in Table 30, Table I, Table J, or Table K) can,for example, be measured by amplifying RNA from a sample usingamplification (NASBA). See, e.g., Kwoh et al., 1989, PNAS USA 86:1173;International Publication No. WO 88/10315; and U.S. Pat. No. 6,329,179,each of which is hereby incorporated by reference. In NASBA, the nucleicacids may be prepared for amplification using conventional methods,e.g., phenol/chloroform extraction, heat denaturation, treatment withlysis buffer and minispin columns for isolation of DNA and RNA orguanidinium chloride extraction of RNA. These amplification techniquesinvolve annealing a primer that has target specific sequences. Followingpolymerization, DNA/RNA hybrids are digested with RNase H while doublestranded DNA molecules are heat denatured again. In either case thesingle stranded DNA is made fully double stranded by addition of secondtarget specific primer, followed by polymerization. The double-strandedDNA molecules are then multiply transcribed by a polymerase such as T7or SP6. In an isothermal cyclic reaction, the RNA's are reversetranscribed into double stranded DNA, and transcribed once with apolymerase such as T7 or SP6. The resulting products, whether truncatedor complete, indicate target specific sequences.

Several techniques may be used to separate amplification products. Forexample, amplification products may be separated by agarose,agarose-acrylamide or polyacrylamide gel electrophoresis usingconventional methods. See Sambrook et al., 2001. Several techniques fordetecting PCR products quantitatively without electrophoresis may alsobe used according to the invention (see, e.g., PCR Protocols, A Guide toMethods and Applications, Innis et al., 1990, Academic Press, Inc. N.Y.,which is hereby incorporated by reference). For example, chromatographictechniques may be employed to effect separation. There are many kinds ofchromatography which may be used in the present invention: adsorption,partition, ion-exchange and molecular sieve, HPLC, and many specializedtechniques for using them including column, paper, thin-layer and gaschromatography (Freifelder, Physical Biochemistry Applications toBiochemistry and Molecular Biology, 2nd ed., Wm. Freeman and Co., NewYork, N.Y., 1982, which is hereby incorporated by reference).

Another example of a separation methodology is to covalently label theoligonucleotide primers used in a PCR reaction with various types ofsmall molecule ligands. In one such separation, a different ligand ispresent on each oligonucleotide. A molecule, perhaps an antibody oravidin if the ligand is biotin, that specifically binds to one of theligands is used to coat the surface of a plate such as a 96 well ELISAplate. Upon application of the PCR reactions to the surface of such aprepared plate, the PCR products are bound with specificity to thesurface. After washing the plate to remove unbound reagents, a solutioncontaining a second molecule that binds to the first ligand is added.This second molecule is linked to some kind of reporter system. Thesecond molecule only binds to the plate if a PCR product has beenproduced whereby both oligonucleotide primers are incorporated into thefinal PCR products. The amount of the PCR product is then detected andquantified in a commercial plate reader much as ELISA reactions aredetected and quantified. An ELISA-like system such as the one describedhere has been developed by Raggio Italgene (under the C-Track tradename.

Amplification products should be visualized in order to confirmamplification of the nucleic acid sequences of interest, i.e., nucleicacid sequences of one or more of the genes described herein (e.g., agene listed in Table 30, Table I, Table J, or Table K). One typicalvisualization method involves staining of a gel with ethidium bromideand visualization under UV light. Alternatively, if the amplificationproducts are integrally labeled with radio- or fluorometrically-labelednucleotides, the amplification products may then be exposed to x-rayfilm or visualized under the appropriate stimulating spectra, followingseparation.

In one embodiment, visualization is achieved indirectly. Followingseparation of amplification products, a labeled, nucleic acid probe isbrought into contact with the amplified nucleic acid sequence ofinterest, i.e., nucleic acid sequences of one or more of the genesdescribed herein (e.g., a gene listed in Table 30, Table I, Table J, orTable K). The probe preferably is conjugated to a chromophore but may beradiolabeled. In another embodiment, the probe is conjugated to abinding partner, such as an antibody or biotin, where the other memberof the binding pair carries a detectable moiety.

In another embodiment, detection is by Southern blotting andhybridization with a labeled probe. The techniques involved in Southernblotting are well known to those of skill in the art and may be found inmany standard books on molecular protocols. See Sambrook et al., 2001.Briefly, amplification products are separated by gel electrophoresis.The gel is then contacted with a membrane, such as nitrocellulose,permitting transfer of the nucleic acid and non-covalent binding.Subsequently, the membrane is incubated with a chromophore-conjugatedprobe that is capable of hybridizing with a target amplificationproduct. Detection is by exposure of the membrane to x-ray film orion-emitting detection devices. One example of the foregoing isdescribed in U.S. Pat. No. 5,279,721, incorporated by reference herein,which discloses an apparatus and method for the automatedelectrophoresis and transfer of nucleic acids. The apparatus permitselectrophoresis and blotting without external manipulation of the geland is ideally suited to carrying out methods according to the presentinvention.

5.4.1.3 Nuclease Protection Assays

In particular embodiments, feature values for biomarkers in a biomarkerprofile can be obtained by performing nuclease protection assays(including both ribonuclease protection assays and S1 nuclease assays)to detect and quantify specific mRNAs (e.g., mRNAs of a gene describedin Table 30, Table I, Table J, or Table K). Such assays are describedin, for example, Sambrook et al., 2001, supra. In nuclease protectionassays, an antisense probe (labeled with, e.g., radiolabeled ornonisotopic) hybridizes in solution to an RNA sample. Followinghybridization, single-stranded, unhybridized probe and RNA are degradedby nucleases. An acrylamide gel is used to separate the remainingprotected fragments. Typically, solution hybridization is more efficientthan membrane-based hybridization, and it can accommodate up to 100 μgof sample RNA, compared with the 20-30 μg maximum of blothybridizations.

The ribonuclease protection assay, which is the most common type ofnuclease protection assay, requires the use of RNA probes.Oligonucleotides and other single-stranded DNA probes can only be usedin assays containing S1 nuclease. The single-stranded, antisense probemust typically be completely homologous to target RNA to preventcleavage of the probe:target hybrid by nuclease.

5.4.1.4 Northern Blot Assays

Any hybridization technique known to those of skill in the art can beused to generate feature values for biomarkers in a biomarker profile.In other particular embodiments, feature values for biomarkers in abiomarker profile can be obtained by Northern blot analysis (to detectand quantify specific RNA molecules (e.g., RNAs of a gene described inTable 30, Table I, Table J, or Table K). A standard Northern blot assaycan be used to ascertain an RNA transcript size, identify alternativelyspliced RNA transcripts, and the relative amounts of one or more genesdescribed herein (in particular, mRNA) in a sample, in accordance withconventional Northern hybridization techniques known to those persons ofordinary skill in the art. In Northern blots, RNA samples are firstseparated by size via electrophoresis in an agarose gel under denaturingconditions. The RNA is then transferred to a membrane, crosslinked andhybridized with a labeled probe. Nonisotopic or high specific activityradiolabeled probes can be used including random-primed,nick-translated, or PCR-generated DNA probes, in vitro transcribed RNAprobes, and oligonucleotides. Additionally, sequences with only partialhomology (e.g., cDNA from a different species or genomic DNA fragmentsthat might contain an exon) may be used as probes. The labeled probe,e.g., a radiolabelled cDNA, either containing the full-length, singlestranded DNA or a fragment of that DNA sequence may be at least 20, atleast 30, at least 50, or at least 100 consecutive nucleotides inlength. The probe can be labeled by any of the many different methodsknown to those skilled in this art. The labels most commonly employedfor these studies are radioactive elements, enzymes, chemicals thatfluoresce when exposed to ultraviolet light, and others. A number offluorescent materials are known and can be utilized as labels. Theseinclude, but are not limited to, fluorescein, rhodamine, auramine, TexasRed, AMCA blue and Lucifer Yellow. The radioactive label can be detectedby any of the currently available counting procedures. Non-limitingexamples of isotopes include ³H, ¹⁴C, ³²P, ³⁵S, ³⁶Cl, ⁵¹Cr, ⁵⁷Co, ⁵⁸Co,⁵⁹Fe, ⁹⁰Y, ¹²⁵I, ¹³¹I, and ¹⁸⁶Re. Enzyme labels are likewise useful, andcan be detected by any of the presently utilized colorimetric,spectrophotometric, fluorospectrophotometric, amperometric or gasometrictechniques. The enzyme is conjugated to the selected particle byreaction with bridging molecules such as carbodiimides, diisocyanates,glutaraldehyde and the like. Any enzymes known to one of skill in theart can be utilized. Examples of such enzymes include, but are notlimited to, peroxidase, beta-D-galactosidase, urease, glucose oxidaseplus peroxidase and alkaline phosphatase. U.S. Pat. Nos. 3,654,090,3,850,752, and 4,016,043 are referred to by way of example for theirdisclosure of alternate labeling material and methods.

5.4.2 Methods of Detecting Proteins

In specific embodiments of the invention, feature values of biomarkersin a biomarker profile can be obtained by detecting proteins, forexample, by detecting the expression product (e.g., a nucleic acid orprotein) of one or more genes described herein (e.g., a gene listed inTable 30, Table I, Table J, or Table K), or post-translationallymodified, or otherwise modified, or processed forms of such proteins. Ina specific embodiment, a biomarker profile is generated by detectingand/or analyzing one or more proteins and/or discriminating fragmentsthereof expressed from a gene disclosed herein (e.g., a gene listed inTable 30, Table I, Table J, or Table K) using any method known to thoseskilled in the art for detecting proteins including, but not limited toprotein microarray analysis, immunohistochemistry and mass spectrometry.

Standard techniques may be utilized for determining the amount of theprotein or proteins of interest (e.g., proteins expressed from geneslisted in Table 30, Table I, Table J, or Table K) present in a sample.For example, standard techniques can be employed using, e.g.,immunoassays such as, for example Western blot, immunoprecipitationfollowed by sodium dodecyl sulfate polyacrylamide gel electrophoresis,(SDS-PAGE), immunocytochemistry, and the like to determine the amount ofprotein or proteins of interest present in a sample. One exemplary agentfor detecting a protein of interest is an antibody capable ofspecifically binding to a protein of interest, preferably an antibodydetectably labeled, either directly or indirectly.

For such detection methods, if desired a protein from the sample to beanalyzed can easily be isolated using techniques which are well known tothose of skill in the art. Protein isolation methods can, for example,be such as those described in Harlow and Lane, 1988, Antibodies: ALaboratory Manual, Cold Spring Harbor Laboratory Press (Cold SpringHarbor, N.Y.), which is incorporated by reference herein in itsentirety.

In certain embodiments, methods of detection of the protein or proteinsof interest involve their detection via interaction with aprotein-specific antibody. For example, antibodies directed to a proteinof interest (e.g., a protein expressed from a gene described herein,e.g., a protein listed in Table 30, Table I, Table J, or Table K).Antibodies can be generated utilizing standard techniques well known tothose of skill in the art. In specific embodiments, antibodies can bepolyclonal, or more preferably, monoclonal. An intact antibody, or anantibody fragment (e.g., scFv, Fab or F(ab′)₂) can, for example, beused.

For example, antibodies, or fragments of antibodies, specific for aprotein of interest can be used to quantitatively or qualitativelydetect the presence of a protein. This can be accomplished, for example,by immunofluorescence techniques. Antibodies (or fragments thereof) can,additionally, be employed histologically, as in immunofluorescence orimmunoelectron microscopy, for in situ detection of a protein ofinterest. In situ detection can be accomplished by removing a biologicalsample (e.g., a biopsy specimen) from a patient, and applying thereto alabeled antibody that is directed to a protein of interest (e.g., aprotein expressed from a gene in Table 30, Table I, Table J, or TableK). The antibody (or fragment) is preferably applied by overlaying theantibody (or fragment) onto a biological sample. Through the use of sucha procedure, it is possible to determine not only the presence of theprotein of interest, but also its distribution, in a particular sample.A wide variety of well-known histological methods (such as stainingprocedures) can be utilized to achieve such in situ detection.

Immunoassays for a protein of interest typically comprise incubating abiological sample of a detectably labeled antibody capable ofidentifying a protein of interest, and detecting the bound antibody byany of a number of techniques well-known in the art. As discussed inmore detail, below, the term “labeled” can refer to direct labeling ofthe antibody via, e.g., coupling (i.e., physically linking) a detectablesubstance to the antibody, and can also refer to indirect labeling ofthe antibody by reactivity with another reagent that is directlylabeled. Examples of indirect labeling include detection of a primaryantibody using a fluorescently labeled secondary antibody.

The biological sample can be brought in contact with and immobilizedonto a solid phase support or carrier such as nitrocellulose, or othersolid support which is capable of immobilizing cells, cell particles orsoluble proteins. The support can then be washed with suitable buffersfollowed by treatment with the detectably labeled fingerprintgene-specific antibody. The solid phase support can then be washed withthe buffer a second time to remove unbound antibody. The amount of boundlabel on solid support can then be detected by conventional methods.

By “solid phase support or carrier” is intended any support capable ofbinding an antigen or an antibody. Well-known supports or carriersinclude glass, polystyrene, polypropylene, polyethylene, dextran, nylon,amylases, natural and modified celluloses, polyacrylamides andmagnetite. The nature of the carrier can be either soluble to someextent or insoluble for the purposes of the present invention. Thesupport material can have virtually any possible structuralconfiguration so long as the coupled molecule is capable of binding toan antigen or antibody. Thus, the support configuration can bespherical, as in a bead, or cylindrical, as in the inside surface of atest tube, or the external surface of a rod. Alternatively, the surfacecan be flat such as a sheet, test strip, etc. Preferred supports includepolystyrene beads. Those skilled in the art will know many othersuitable carriers for binding antibody or antigen, or will be able toascertain the same by use of routine experimentation.

One of the ways in which an antibody specific for a protein of interestcan be detectably labeled is by linking the same to an enzyme and use inan enzyme immunoassay (EIA) (Voller, 1978, “The Enzyme LinkedImmunosorbent Assay (ELISA)”, Diagnostic Horizons 2:1-7, MicrobiologicalAssociates Quarterly Publication, Walkersville, Md.; Voller et al.,1978, J. Clin. Pathol. 31:507-520; Butler, J. E., 1981, Meth. Enzymol.73:482-523; Maggio (ed.), 1980, Enzyme Immunoassay, CRC Press, BocaRaton, Fla.; Ishikawa et al., (eds.), 1981, Enzyme Immunoassay, KgakuShoin, Tokyo, each of which is hereby incorporated by reference in itsentirety). The enzyme which is bound to the antibody will react with anappropriate substrate, preferably a chromogenic substrate, in such amanner as to produce a chemical moiety which can be detected, forexample, by spectrophotometric, fluorimetric or by visual means. Enzymeswhich can be used to detectably label the antibody include, but are notlimited to, malate dehydrogenase, staphylococcal nuclease,delta-5-steroid isomerase, yeast alcohol dehydrogenase,alpha-glycerophosphate, dehydrogenase, triose phosphate isomerase,horseradish peroxidase, alkaline phosphatase, asparaginase, glucoseoxidase, beta-galactosidase, ribonuclease, urease, catalase,glucose-6-phosphate dehydrogenase, glucoamylase andacetylcholinesterase. The detection can be accomplished by colorimetricmethods which employ a chromogenic substrate for the enzyme. Detectioncan also be accomplished by visual comparison of the extent of enzymaticreaction of a substrate in comparison with similarly prepared standards.

Detection can also be accomplished using any of a variety of otherimmunoassays. For example, by radioactively labeling the antibodies orantibody fragments, it is possible to detect a protein of interestthrough the use of a radioimmunoassay (MA) (see, for example, Weintraub,1986, Principles of Radioimmunoassays, Seventh Training Course onRadioligand Assay Techniques, The Endocrine Society, which is herebyincorporated by reference herein). The radioactive isotope (e.g. ¹²⁵I,¹³¹I, ³⁵S or ³H) can be detected by such means as the use of a gammacounter or a scintillation counter or by autoradiography.

It is also possible to label the antibody with a fluorescent compound.When the fluorescently labeled antibody is exposed to light of theproper wavelength, its presence can then be detected due tofluorescence. Among the most commonly used fluorescent labelingcompounds are fluorescein isothiocyanate, rhodamine, phycoerythrin,phycocyanin, allophycocyanin, o-phthaldehyde and fluorescamine.

The antibody can also be detectably labeled using fluorescence emittingmetals such as ¹⁵²Eu, or others of the lanthanide series. These metalscan be attached to the antibody using such metal chelating groups asdiethylenetriaminepentacetic acid (DTPA) or ethylenediaminetetraaceticacid (EDTA).

The antibody also can be detectably labeled by coupling it to achemiluminescent compound. The presence of the chemiluminescent-taggedantibody is then determined by detecting the presence of luminescencethat arises during the course of a chemical reaction. Examples ofparticularly useful chemiluminescent labeling compounds are luminol,isoluminol, theromatic acridinium ester, imidazole, acridinium salt andoxalate ester.

Likewise, a bioluminescent compound can be used to label the antibody ofthe present invention. Bioluminescence is a type of chemiluminescencefound in biological systems in, which a catalytic protein increases theefficiency of the chemiluminescent reaction. The presence of abioluminescent protein is determined by detecting the presence ofluminescence. Important bioluminescent compounds for purposes oflabeling are luciferin, luciferase and aequorin.

In another embodiment, specific binding molecules other than antibodies,such as aptamers, may be used to bind the biomarkers. In yet anotherembodiment, the biomarker profile may comprise a measurable aspect of aninfectious agent (e.g., lipopolysaccharides or viral proteins) or acomponent thereof.

In some embodiments, a protein chip assay (e.g., The ProteinChip®Biomarker System, Ciphergen, Fremont, Calif.) is used to measure featurevalues for the biomarkers in the biomarker profile. See also, forexample, Lin, 2004, Modern Pathology, 1-9; Li, 2004, Journal of Urology171, 1782-1787; Wadsworth, 2004, Clinical Cancer Research, 10,1625-1632; Prieto, 2003, Journal of Liquid Chromatography & RelatedTechnologies 26, 2315-2328; Coombes, 2003, Clinical Chemistry 49,1615-1623; Mian, 2003, Proteomics 3, 1725-1737; Lehre et al., 2003, BJUInternational 92, 223-225; and Diamond, 2003, Journal of the AmericanSociety for Mass Spectrometry 14, 760-765, each of which is herebyincorporated by reference in its entirety.

In some embodiments, a bead assay is used to measure feature values forthe biomarkers in the biomarker profile. One such bead assay is theBecton Dickinson Cytometric Bead Array (CBA). CBA employs a series ofparticles with discrete fluorescence intensities to simultaneouslydetect multiple soluble analytes. CBA is combined with flow cytometry tocreate a multiplexed assay. The Becton Dickinson CBA system, as embodiedfor example in the Becton Dickinson Human Inflammation Kit, uses thesensitivity of amplified fluorescence detection by flow cytometry tomeasure soluble analytes in a particle-based immunoassay. Each bead in aCBA provides a capture surface for a specific protein and is analogousto an individually coated well in an ELISA plate. The BD CBA capturebead mixture is in suspension to allow for the detection of multipleanalytes in a small volume sample.

In some embodiments the multiplex analysis method described in U.S. Pat.No. 5,981,180 (“the '180 patent”), herein incorporated by reference inits entirety, and in particular for its teachings of the generalmethodology, bead technology, system hardware and antibody detection, isused to measure feature values for the biomarkers in a biomarkerprofile. For this analysis, a matrix of microparticles is synthesized,where the matrix consists of different sets of microparticles. Each setof microparticles can have thousands of molecules of a distinct antibodycapture reagent immobilized on the microparticle surface and can becolor-coded by incorporation of varying amounts of two fluorescent dyes.The ratio of the two fluorescent dyes provides a distinct emissionspectrum for each set of microparticles, allowing the identification ofa microparticle a set following the pooling of the various sets ofmicroparticles. U.S. Pat. Nos. 6,268,222 and 6,599,331 also areincorporated herein by reference in their entirety, and in particularfor their teachings of various methods of labeling microparticles formultiplex analysis.

5.4.3 Use of Other Methods of Detection

In some embodiments, a separation method may be used determine featurevalues for biomarkers in a biomarker profile, such that only a subset ofbiomarkers within the sample is analyzed. For example, the biomarkersthat are analyzed in a sample may be mRNA species from a cellularextract which has been fractionated to obtain only the nucleic acidbiomarkers within the sample, or the biomarkers may be from a fractionof the total complement of proteins within the sample, which have beenfractionated by chromatographic techniques.

Feature values for biomarkers in a biomarker profile can also, forexample, be generated by the use of one or more of the following methodsdescribed below. For example, methods may include nuclear magneticresonance (NMR) spectroscopy, a mass spectrometry method, such aselectrospray ionization mass spectrometry (ESI-MS), ESI-MS/MS,ESI-MS/(MS)^(n) (n is an integer greater than zero), matrix-assistedlaser desorption ionization time-of-flight mass spectrometry(MALDI-TOF-MS), surface-enhanced laser desorption/ionizationtime-of-flight mass spectrometry (SELDI-TOF-MS), desorption/ionizationon silicon (DIOS), secondary ion mass spectrometry (SIMS), quadrupoletime-of-flight (Q-TOF), atmospheric pressure chemical ionization massspectrometry (APCI-MS), APCI-MS/MS, APCI-(MS)^(n), atmospheric pressurephotoionization mass spectrometry (APPI-MS), APPI-MS/MS, andAPPI-(MS)^(n). Other mass spectrometry methods may include, inter alia,quadrupole, Fourier transform mass spectrometry (FTMS) and ion trap.Other suitable methods may include chemical extraction partitioning,column chromatography, ion exchange chromatography, hydrophobic (reversephase) liquid chromatography, isoelectric focusing, one-dimensionalpolyacrylamide gel electrophoresis (PAGE), two-dimensionalpolyacrylamide gel electrophoresis (2D-PAGE) or other chromatography,such as thin-layer, gas or liquid chromatography, or any combinationthereof. In one embodiment, the biological sample may be fractionatedprior to application of the separation method.

In one embodiment, laser desorption/ionization time-of-flight massspectrometry is used to create determine feature values in a biomarkerprofile where the biomarkers are proteins or protein fragments that havebeen ionized and vaporized off an immobilizing support by incident laserradiation and the feature values are the presence or absence of peaksrepresenting these fragments in the mass spectra profile. A variety oflaser desorption/ionization techniques are known in the art (see, e.g.,Guttman et al., 2001, Anal. Chem. 73:1252-62 and Wei et al., 1999,Nature 399:243-246, each of which is hereby incorporated by herein bereference in its entirety).

Laser desorption/ionization time-of-flight mass spectrometry allows thegeneration of large amounts of information in a relatively short periodof time. A biological sample is applied to one of several varieties of asupport that binds all of the biomarkers, or a subset thereof, in thesample. Cell lysates or samples are directly applied to these surfacesin volumes as small as 0.5 μL, with or without prior purification orfractionation. The lysates or sample can be concentrated or dilutedprior to application onto the support surface. Laserdesorption/ionization is then used to generate mass spectra of thesample, or samples, in as little as three hours.

5.5 Data Analysis Algorithms

Biomarkers whose corresponding feature values are capable ofdiscriminating between converters and nonconverters are identified inthe present invention. The identity of these biomarkers and theircorresponding features (e.g., expression levels) can be used to developa decision rule, or plurality of decision rules, that discriminatebetween converters and nonconverters. Section 6 below illustrates howdata analysis algorithms can be used to construct a number of suchdecision rules. Each of the data analysis algorithms described inSection 6 use features (e.g., expression values) of a subset of thebiomarkers identified in the present invention across a trainingpopulation that includes converters and nonconverters. Typically, a SIRSsubject is considered a nonconverter when the subject does not developsepsis in a defined time period (e.g., observation period). This definedtime period can be, for example, twelve hours, twenty four hours,forty-eight hours, a day, a week, a month, or longer. Specific dataanalysis algorithms for building a decision rule, or plurality ofdecision rules, that discriminate between subjects that develop sepsisand subjects that do not develop sepsis during a defined period will bedescribed in the subsections below. Once a decision rule has been builtusing these exemplary data analysis algorithms or other techniques knownin the art, the decision rule can be used to classify a test subjectinto one of the two or more phenotypic classes (e.g., a converter or anonconverter). This is accomplished by applying the decision rule to abiomarker profile obtained from the test subject. Such decision rules,therefore, have enormous value as diagnostic indicators.

The present invention provides, in one aspect, for the evaluation of abiomarker profile from a test subject to biomarker profiles obtainedfrom a training population. In some embodiments, each biomarker profileobtained from subjects in the training population, as well as the testsubject, comprises a feature for each of a plurality of differentbiomarkers. In some embodiments, this comparison is accomplished by (i)developing a decision rule using the biomarker profiles from thetraining population and (ii) applying the decision rule to the biomarkerprofile from the test subject. As such, the decision rules applied insome embodiments of the present invention are used to determine whethera test subject having SIRS will or will not likely acquire sepsis.

In some embodiments of the present invention, when the results of theapplication of a decision rule indicate that the subject will likelyacquire sepsis, the subject is diagnosed as a “sepsis” subject. If theresults of an application of a decision rule indicate that the subjectwill not acquire sepsis, the subject is diagnosed as a “SIRS” subject.Thus, in some embodiments, the result in the above-described binarydecision situation has four possible outcomes:

(i) truly septic, where the decision rule indicates that the subjectwill acquire sepsis and the subject does in fact acquire sepsis duringthe definite time period (true positive, TP);

(ii) falsely septic, where the decision rule indicates that the subjectwill acquire sepsis and the subject, in fact, does not acquire sepsisduring the definite time period (false positive, FP);

(iii) truly SIRS, where the decision rule indicates that the subjectwill not acquire sepsis and the subject, in fact, does not acquiresepsis during the definite time period (true negative, TN); or

(iv) falsely SIRS, where the decision rule indicates that the subjectwill not acquire sepsis and the subject, in fact, does acquire sepsisduring the definite time period (false negative, FN).

It will be appreciated that other definitions for TP, FP, TN, FN can bemade. For example, TP could have been defined as instances where thedecision rule indicates that the subject will not acquire sepsis and thesubject, in fact, does not acquire sepsis during the definite timeperiod. While all such alternative definitions are within the scope ofthe present invention, for ease of understanding the present invention,the definitions for TP, FP, TN, and FN given by definitions (i) through(iv) above will be used herein, unless otherwise stated.

As will be appreciated by those of skill in the art, a number ofquantitative criteria can be used to communicate the performance of thecomparisons made between a test biomarker profile and referencebiomarker profiles (e.g., the application of a decision rule to thebiomarker profile from a test subject). These include positive predictedvalue (PPV), negative predicted value (NPV), specificity, sensitivity,accuracy, and certainty. In addition, other constructs such a receiveroperator curves (ROC) can be used to evaluate decision rule performance.As used herein:

${PPV} = \frac{TP}{{TP} + {FP}}$ ${NPV} = \frac{TN}{{TN} + {FN}}$${specificity} = \frac{TN}{{TN} + {FP}}$${sensitivity} = \frac{TP}{{TP} + {FN}}$${accuracy} = {{certainty} = \frac{{TP} + {TN}}{N}}$

Here, N is the number of samples compared (e.g., the number of testsamples for which a determination of sepsis or SIRS is sought). Forexample, consider the case in which there are ten subjects for whichSIRS/sepsis classification is sought. Biomarker profiles are constructedfor each of the ten test subjects. Then, each of the biomarker profilesis evaluated by applying a decision rule, where the decision rule wasdeveloped based upon biomarker profiles obtained from a trainingpopulation. In this example, N, from the above equations, is equal to10. Typically, N is a number of samples, where each sample was collectedfrom a different member of a population. This population can, in fact,be of two different types. In one type, the population comprisessubjects whose samples and phenotypic data (e.g., feature values ofbiomarkers and an indication of whether or not the subject acquiredsepsis) was used to construct or refine a decision rule. Such apopulation is referred to herein as a training population. In the othertype, the population comprises subjects that were not used to constructthe decision rule. Such a population is referred to herein as avalidation population. Unless otherwise stated, the populationrepresented by N is either exclusively a training population orexclusively a validation population, as opposed to a mixture of the twopopulation types. It will be appreciated that scores such as accuracywill be higher (closer to unity) when they are based on a trainingpopulation as opposed to a validation population. Nevertheless, unlessotherwise explicitly stated herein, all criteria used to assess theperformance of a decision rule (or other forms of evaluation of abiomarker profile from a test subject) including certainty (accuracy)refer to criteria that were measured by applying the decision rulecorresponding to the criteria to either a training population or avalidation population. Furthermore, the definitions for PPV, NPV,specificity, sensitivity, and accuracy defined above can also be foundin Draghici, Data Analysis Tools for DNA Microanalysis, 2003, CRC PressLLC, Boca Raton, Fla., pp. 342-343, which is hereby incorporated hereinby reference.

In some embodiments the training population comprises nonconverters andconverters. In some embodiments, biomarker profiles are constructed fromthis population using biological samples collected from the trainingpopulation at some time period prior to the onset of sepsis by theconverters of the population. As such, for the converters of thetraining population, a biological sample can be collected two weekbefore, one week before, four days before, three days before, one daybefore, or any other time period before the converters became septic. Inpractice, such collections are obtained by collecting a biologicalsample at regular time intervals after admittance into the hospital witha SIRS diagnosis. For example, in one approach, subjects who have beendiagnosed with SIRS in a hospital are used as a training population.Once admitted to the hospital with SIRS, the biological samples arecollected from the subjects at selected times (e.g., hourly, every eighthours, every twelve hours, daily, etc.). A portion of the subjectsacquire sepsis and a portion of the subjects do not acquire sepsis. Forthe subjects that acquire sepsis, the biological sample taken from thesubjects just prior to the onset of sepsis are termed the T⁻¹²biological samples. All other biological samples from the subjects areretroactively indexed relative to these biological samples. Forinstance, when a biological sample has been taken from a subject on adaily basis, the biological sample taken the day before the T⁻¹² sampleis referred to as the T⁻³⁶ biological sample. Time points for biologicalsamples for a nonconverter in the training population are identified by“time-matching” the nonconverter subject with a converter subject. Toillustrate, consider the case in which a subject in the trainingpopulation became clinically-defined as septic on his sixth day ofenrollment. For the sake of illustration, for this subject, T⁻³⁶ is dayfour of the study, and the T⁻³⁶ biological sample is the biologicalsample that was obtained on day four of the study. Likewise, T⁻³⁶ forthe matched nonconverter subject is deemed to be day four of the studyon this paired nonconverter subject.

In some embodiments, N is more than one, more than five, more than ten,more than twenty, between ten and 100, more than 100, or less than 1000subjects. A decision rule (or other forms of comparison) can have atleast about 99% certainty, or even more, in some embodiments, against atraining population or a validation population. In other embodiments,the certainty is at least about 97%, at least about 95%, at least about90%, at least about 85%, at least about 80%, at least about 75%, atleast about 70%, at least about 65%, or at least about 60% against atraining population or a validation population (and therefore against asingle subject that is not part of a training population such as aclinical patient). The useful degree of certainty may vary, depending onthe particular method of the present invention. As used herein,“certainty” means “accuracy.” In one embodiment, the sensitivity and/orspecificity is at is at least about 97%, at least about 95%, at leastabout 90%, at least about 85%, at least about 80%, at least about 75%,or at least about 70% against a training population or a validationpopulation. In some embodiments, such decision rules are used to predictthe development of sepsis with the stated accuracy. In some embodiments,such decision rules are used to diagnoses sepsis with the statedaccuracy. In some embodiments, such decision rules are used to determinea stage of sepsis with the stated accuracy.

The number of features that may be used by a decision rule to classify atest subject with adequate certainty is two or more. In someembodiments, it is three or more, four or more, ten or more, or between10 and 200. Depending on the degree of certainty sought, however, thenumber of features used in a decision rule can be more or less, but inall cases is at least two. In one embodiment, the number of featuresthat may be used by a decision rule to classify a test subject isoptimized to allow a classification of a test subject with highcertainty.

In some of the examples in Section 6 below, microarray data abundancedata was collected for a plurality of biomarkers in each subject. Thatis, for each biomarker in a biomarker profile, a feature, microarrayabundance data for the biomarker, was measured. Decision rules aredeveloped from such biomarker profiles from a training population usingdata analysis algorithms in order to predict sample phenotypes based onobserved gene expression patterns. While new and microarray specificclassification tools are constantly being developed, the existing bodyof pattern recognition and prediction algorithms provide effective dataanalysis algorithms for constructing decision rules. See, for example,National Research Council; Panel on Discriminant Analysis Classificationand Clustering, Discriminant Analysis and Clustering, Washington, D.C.:National Academy Press, which is hereby incorporated by reference.Furthermore, the techniques described in Dudoit et al., 2002,“Comparison of discrimination methods for the classification of tumorsusing gene expression data.” JASA 97; 77-87, hereby incorporated byreference herein in its entirety, can be used to develop such decisionrules.

Relevant data analysis algorithms for developing a decision ruleinclude, but are not limited to, discriminant analysis including linear,logistic, and more flexible discrimination techniques (see, e.g.,Gnanadesikan, 1977, Methods for Statistical Data Analysis ofMultivariate Observations, New York: Wiley 1977, which is herebyincorporated by reference herein in its entirety); tree-based algorithmssuch as classification and regression trees (CART) and variants (see,e.g., Breiman, 1984, Classification and Regression Trees, Belmont,Calif.: Wadsworth International Group, which is hereby incorporated byreference herein in its entirety, as well as Section 5.1.3, below);generalized additive models (see, e.g., Tibshirani, 1990, GeneralizedAdditive Models, London: Chapman and Hall, which is hereby incorporatedby reference herein in its entirety); and neural networks (see, e.g.,Neal, 1996, Bayesian Learning for Neural Networks, New York:Springer-Verlag; and Insua, 1998, Feedforward neural networks fornonparametric regression In: Practical Nonparametric and SemiparametricBayesian Statistics, pp. 181-194, New York: Springer, which is herebyincorporated by reference herein in its entirety, as well as Section5.5.6, below).

In one embodiment, comparison of a test subject's biomarker profile to abiomarker profiles obtained from a training population is performed, andcomprises applying a decision rule. The decision rule is constructedusing a data analysis algorithm, such as a computer pattern recognitionalgorithm. Other suitable data analysis algorithms for constructingdecision rules include, but are not limited to, logistic regression (seeSection 5.5.10, below) or a nonparametric algorithm that detectsdifferences in the distribution of feature values (e.g., a WilcoxonSigned Rank Test (unadjusted and adjusted)). The decision rule can bebased upon two, three, four, five, 10, 20 or more features,corresponding to measured observables from one, two, three, four, five,10, 20 or more biomarkers. In one embodiment, the decision rule is basedon hundreds of features or more. Decision rules may also be built usinga classification tree algorithm. For example, each biomarker profilefrom a training population can comprise at least three features, wherethe features are predictors in a classification tree algorithm (seeSection 5.5.1, below). The decision rule predicts membership within apopulation (or class) with an accuracy of at least about at least about70%, of at least about 75%, of at least about 80%, of at least about85%, of at least about 90%, of at least about 95%, of at least about97%, of at least about 98%, of at least about 99%, or about 100%.

Suitable data analysis algorithms are known in the art, some of whichare reviewed in Hastie et al., supra. In a specific embodiment, a dataanalysis algorithm of the invention comprises Classification andRegression Tree (CART; Section 5.5.1, below), Multiple AdditiveRegression Tree (MART; Section 5.5.4, below), Prediction Analysis forMicroarrays (PAM; Section 5.5.2, below) or Random Forest analysis(Section 5.5.1, below). Such algorithms classify complex spectra frombiological materials, such as a blood sample, to distinguish subjects asnormal or as possessing biomarker expression levels characteristic of aparticular disease state. In other embodiments, a data analysisalgorithm of the invention comprises ANOVA and nonparametricequivalents, linear discriminant analysis (Section 5.5.10, below),logistic regression analysis (Section 5.5.10, below), nearest neighborclassifier analysis (Section 5.5.9, below), neural networks (Section5.5.6, below), principal component analysis (Section 5.5.8, below),quadratic discriminant analysis (Section 5.5.11, below), regressionclassifiers (Section 5.5.5, below) and support vector machines (Section5.5.12, below). While such algorithms may be used to construct adecision rule and/or increase the speed and efficiency of theapplication of the decision rule and to avoid investigator bias, one ofordinary skill in the art will realize that computer-based algorithmsare not required to carry out the methods of the present invention.

Decision rules can be used to evaluate biomarker profiles, regardless ofthe method that was used to generate the biomarker profile. For example,suitable decision rules that can be used to evaluate biomarker profilesgenerated using gas chromatography, as discussed in Harper, “Pyrolysisand GC in Polymer Analysis,” Dekker, New York (1985). Further, Wagner etal., 2002, Anal. Chem. 74:1824-1835 disclose a decision rule thatimproves the ability to classify subjects based on spectra obtained bystatic time-of-flight secondary ion mass spectrometry (TOF-SIMS).Additionally, Bright et al., 2002, J. Microbiol. Methods 48:127-38,hereby incorporated by reference herein in its entirety, disclose amethod of distinguishing between bacterial strains with high certainty(79-89% correct classification rates) by analysis of MALDI-TOF-MSspectra. Dalluge, 2000, Fresenius J. Anal. Chem. 366:701-711, herebyincorporated by reference herein in its entirety, discusses the use ofMALDI-TOF-MS and liquid chromatography-electrospray ionization massspectrometry (LC/ESI-MS) to classify profiles of biomarkers in complexbiological samples.

5.5.1 Decision Trees

One type of decision rule that can be constructed using the featurevalues of the biomarkers identified in the present invention is adecision tree. Here, the “data analysis algorithm” is any technique thatcan build the decision tree, whereas the final “decision tree” is thedecision rule. A decision tree is constructed using a trainingpopulation and specific data analysis algorithms. Decision trees aredescribed generally by Duda, 2001, Pattern Classification, John Wiley &Sons, Inc., New York. pp. 395-396, which is hereby incorporated byreference. Tree-based methods partition the feature space into a set ofrectangles, and then fit a model (like a constant) in each one.

The training population data includes the features (e.g., expressionvalues, or some other observable) for the biomarkers of the presentinvention across a training set population. One specific algorithm thatcan be used to construct a decision tree is a classification andregression tree (CART). Other specific decision tree algorithms include,but are not limited to, ID3, C4.5, MART, and Random Forests. CART, ID3,and C4.5 are described in Duda, 2001, Pattern Classification, John Wiley& Sons, Inc., New York. pp. 396-408 and pp. 411-412, which is herebyincorporated by reference. CART, MART, and C4.5 are described in Hastieet al., 2001, The Elements of Statistical Learning, Springer-Verlag, NewYork, Chapter 9, which is hereby incorporated by reference in itsentirety. Random Forests are described in Breiman, 1999, “RandomForests-Random Features,” Technical Report 567, Statistics Department,U.C. Berkeley, September 1999, which is hereby incorporated by referencein its entirety.

In some embodiments of the present invention, decision trees are used toclassify subjects using features for combinations of biomarkers of thepresent invention. Decision tree algorithms belong to the class ofsupervised learning algorithms. The aim of a decision tree is to inducea classifier (a tree) from real-world example data. This tree can beused to classify unseen examples that have not been used to derive thedecision tree. As such, a decision tree is derived from training data.Exemplary training data contains data for a plurality of subjects (thetraining population). For each respective subject there is a pluralityof features the class of the respective subject (e.g., sepsis/SIRS). Inone embodiment of the present invention, the training data is expressiondata for a combination of biomarkers across the training population.

The following algorithm describes an exemplary decision tree derivation:

Tree(Examples,Class,Features) Create a root node If all Examples havethe same Class value, give the root this label Else if Features is emptylabel the root according to the most common value Else begin  Calculatethe information gain for each Feature  Select the Feature A with highestinformation gain  and make this the root  Feature  For each possiblevalue, v, of this Feature   Add a new branch below the root,corresponding to A = v   Let Examples(v) be those examples with A = v  If Examples(v) is empty, make the new branch a leaf   node labeled  with the most common value among Examples   Else let the new branch bethe tree created by   Tree(Examples(v),Class,Features - {A}) end

A more detailed description of the calculation of information gain isshown in the following. If the possible classes v_(i) of the exampleshave probabilities P(v_(i)) then the information content I of the actualanswer is given by:

${I\left( {{P\left( v_{1} \right)},\ldots \mspace{14mu},{P\left( v_{n} \right)}} \right)} = {\sum\limits_{i = 1}^{n}\; {{- {P\left( v_{1} \right)}}\log_{2}{P\left( v_{i} \right)}}}$

The I-value shows how much information we need in order to be able todescribe the outcome of a classification for the specific dataset used.Supposing that the dataset contains p positive (e.g. will developsepsis) and n negative (e.g. will not develop sepsis) examples (e.g.subjects), the information contained in a correct answer is:

${I\left( {\frac{p}{p + n} \cdot \frac{n}{p + n}} \right)} = {{{- \frac{p}{p + n}}\log_{2}\frac{p}{p + n}} - {\frac{n}{p + n}\log_{2}\frac{n}{p + n}}}$

where log₂ is the logarithm using base two. By testing single featuresthe amount of information needed to make a correct classification can bereduced. The remainder for a specific feature A (e.g. representing aspecific biomarker) shows how much the information that is needed can bereduced.

${{Remainder}(A)} = {\sum\limits_{i = 1}^{v}\; {\frac{p_{i} + n_{i}}{p + n}{I\left( {\frac{p_{i}}{p_{i} + n_{i}} \cdot \frac{n_{i}}{p_{i} + n_{i}}} \right)}}}$

“v” is the number of unique attribute values for feature A in a certaindataset, “i” is a certain attribute value, “p_(i)” is the number ofexamples for feature A where the classification is positive (e.g. willdevelop sepsis), “n_(i)” is the number of examples for feature A wherethe classification is negative (e.g. will not develop sepsis).

The information gain of a specific feature A is calculated as thedifference between the information content for the classes and theremainder of feature A:

${{Gain}(A)} = {{I\left( {\frac{p}{p + n} \cdot \frac{n}{p + n}} \right)} - {{Remainder}(A)}}$

The information gain is used to evaluate how important the differentfeatures are for the classification (how well they split up theexamples), and the feature with the highest information.

In general there are a number of different decision tree algorithms,many of which are described in Duda, Pattern Classification, SecondEdition, 2001, John Wiley & Sons, Inc. Decision tree algorithms oftenrequire consideration of feature processing, impurity measure, stoppingcriterion, and pruning. Specific decision tree algorithms include, butare not limited to classification and regression trees (CART),multivariate decision trees, ID3, and C4.5.

In one approach, when a decision tree is used, the gene expression datafor a select combination of genes described in the present inventionacross a training population is standardized to have mean zero and unitvariance. The members of the training population are randomly dividedinto a training set and a test set. For example, in one embodiment, twothirds of the members of the training population are placed in thetraining set and one third of the members of the training population areplaced in the test set. The expression values for a select combinationof biomarkers described in the present invention is used to constructthe decision tree. Then, the ability for the decision tree to correctlyclassify members in the test set is determined. In some embodiments,this computation is performed several times for a given combination ofbiomarkers. In each computational iteration, the members of the trainingpopulation are randomly assigned to the training set and the test set.Then, the quality of the combination of biomarkers is taken as theaverage of each such iteration of the decision tree computation.

In addition to univariate decision trees in which each split is based ona feature value for a corresponding biomarker, among the set ofbiomarkers of the present invention, or the relative feature values oftwo such biomarkers, multivariate decision trees can be implemented as adecision rule. In such multivariate decision trees, some or all of thedecisions actually comprise a linear combination of feature values for aplurality of biomarkers of the present invention. Such a linearcombination can be trained using known techniques such as gradientdescent on a classification or by the use of a sum-squared-errorcriterion. To illustrate such a decision tree, consider the expression:

0.04x ₁+0.16x ₂<500

Here, x₁ and x₂ refer to two different features for two differentbiomarkers from among the biomarkers of the present invention. To pollthe decision rule, the values of features x₁ and x₂ are obtained fromthe measurements obtained from the unclassified subject. These valuesare then inserted into the equation. If a value of less than 500 iscomputed, then a first branch in the decision tree is taken. Otherwise,a second branch in the decision tree is taken. Multivariate decisiontrees are described in Duda, 2001, Pattern Classification, John Wiley &Sons, Inc., New York, pp. 408-409, which is hereby incorporated byreference.

Another approach that can be used in the present invention ismultivariate adaptive regression splines (MARS). MARS is an adaptiveprocedure for regression, and is well suited for the high-dimensionalproblems addressed by the present invention. MARS can be viewed as ageneralization of stepwise linear regression or a modification of theCART method to improve the performance of CART in the regressionsetting. MARS is described in Hastie et al., 2001, The Elements ofStatistical Learning, Springer-Verlag, New York, pp. 283-295, which ishereby incorporated by reference in its entirety.

5.5.2 Predictive Analysis of Microarrays (PAM)

One approach to developing a decision rule using feature values ofbiomarkers of the present invention is the nearest centroid classifier.Such a technique computes, for each class (sepsis and SIRS), a centroidgiven by the average feature levels of the biomarkers in the class, andthen assigns new samples to the class whose centroid is nearest. Thisapproach is similar to k-means clustering except clusters are replacedby known classes. This algorithm can be sensitive to noise when a largenumber of biomarkers are used. One enhancement to the technique usesshrinkage: for each biomarker, differences between class centroids areset to zero if they are deemed likely to be due to chance. This approachis implemented in the Prediction Analysis of Microarray, or PAM. See,for example, Tibshirani et al., 2002, Proceedings of the NationalAcademy of Science USA 99; 6567-6572, which is hereby incorporated byreference in its entirety. Shrinkage is controlled by a threshold belowwhich differences are considered noise. Biomarkers that show nodifference above the noise level are removed. A threshold can be chosenby cross-validation. As the threshold is decreased, more biomarkers areincluded and estimated classification errors decrease, until they reacha bottom and start climbing again as a result of noise biomarkers—aphenomenon known as overfitting.

5.5.3 Bagging, Boosting, and the Random Subspace Method

Bagging, boosting, the random subspace method, and additive trees aredata analysis algorithms known as combining techniques that can be usedto improve weak decision rules. These techniques are designed for, andusually applied to, decision trees, such as the decision trees describedin Section 5.5.1, above. In addition, such techniques can also be usefulin decision rules developed using other types of data analysisalgorithms such as linear discriminant analysis.

In bagging, one samples the training set, generating random independentbootstrap replicates, constructs the decision rule on each of these, andaggregates them by a simple majority vote in the final decision rule.See, for example, Breiman, 1996, Machine Learning 24, 123-140; and Efron& Tibshirani, An Introduction to Boostrap, Chapman & Hall, New York,1993, which is hereby incorporated by reference in its entirety.

In boosting, decision rules are constructed on weighted versions of thetraining set, which are dependent on previous classification results.Initially, all features under consideration have equal weights, and thefirst decision rule is constructed on this data set. Then, weights arechanged according to the performance of the decision rule. Erroneouslyclassified features get larger weights, and the next decision rule isboosted on the reweighted training set. In this way, a sequence oftraining sets and decision rules is obtained, which is then combined bysimple majority voting or by weighted majority voting in the finaldecision rule. See, for example, Freund & Schapire, “Experiments with anew boosting algorithm,” Proceedings 13th International Conference onMachine Learning, 1996, 148-156, which is hereby incorporated byreference in its entirety.

To illustrate boosting, consider the case where there are two phenotypesexhibited by the population under study, phenotype 1 (e.g., acquiringsepsis during a defined time period), and phenotype 2 (e.g., SIRS only,meaning that the subject does acquire sepsis within a defined timeperiod). Given a vector of predictor biomarkers (e.g., a vector offeatures that represent such biomarkers) from the training set data, adecision rule G(X) produces a prediction taking one of the type valuesin the two value set: {phenotype 1, phenotype 2}. The error rate on thetraining sample is

$\overset{\_}{err} = {\frac{1}{N}{\sum\limits_{i = 1}^{N}\; {I\left( {y_{i} \neq {G\left( x_{i} \right)}} \right)}}}$

where N is the number of subjects in the training set (the sum total ofthe subjects that have either phenotype 1 or phenotype 2). For example,if there are 49 organisms that acquire sepsis and 72 organisms thatremain in the SIRS state, N is 121. A weak decision rule is one whoseerror rate is only slightly better than random guessing. In the boostingalgorithm, the weak decision rule is repeatedly applied to modifiedversions of the data, thereby producing a sequence of weak decisionrules G_(m)(x), m, =1, 2, . . . , M. The predictions from all of thedecision rules in this sequence are then combined through a weightedmajority vote to produce the final decision rule:

${G(x)} = {{sign}\left( {\sum\limits_{m = 1}^{M}\; {\alpha_{m}{G_{m}(x)}}} \right)}$

Here α₁, α₂, . . . , α_(M) are computed by the boosting algorithm andtheir purpose is to weigh the contribution of each respective decisionrule Gm(x). Their effect is to give higher influence to the moreaccurate decision rules in the sequence.

The data modifications at each boosting step consist of applying weightsw₁, w₂, . . . , w_(n) to each of the training observations (x_(i),y_(i)), i=1, 2, . . . , N. Initially all the weights are set tow_(i)=1/N, so that the first step simply trains the decision rule on thedata in the usual manner. For each successive iteration m=2, 3, . . . ,M the observation weights are individually modified and the decisionrule is reapplied to the weighted observations. At step m, thoseobservations that were misclassified by the decision rule G_(m)−1(x)induced at the previous step have their weights increased, whereas theweights are decreased for those that were classified correctly. Thus asiterations proceed, observations that are difficult to correctlyclassify receive ever-increasing influence. Each successive decisionrule is thereby forced to concentrate on those training observationsthat are missed by previous ones in the sequence.

The exemplary boosting algorithm is summarized as follows:

1. Initialize the observation weights w_(i) = 1/N, i =1, 2, . . . , N.2. For m = 1 to M: (a) Fit a decision rule G_(m)(x) to the training setusing weights w_(i). (b) Compute${err}_{m} = \frac{\sum\limits_{i = 1}^{N}\; {w_{i}{I\left( {y_{i} \neq {G_{m}\left( x_{i} \right)}} \right)}}}{\sum\limits_{i = 1}^{N}\; w_{i}}$(c) Compute α_(m) = log((l − err_(m))/err_(m)). (d) Set w_(i)←w_(i) ·α_(m) · I(y_(i) ≠ G_(m)(x_(i)))], i = 1, 2, . . . , N. 3. Output G(x) =sign └Σ ^(m=1) ^(M) α_(m)G_(m)(x)┘

In one embodiment in accordance with this algorithm, each object is, infact, a factor. Furthermore, in the algorithm, the current decision ruleG_(m)(x) is induced on the weighted observations at line 2a. Theresulting weighted error rate is computed at line 2b. Line 2c calculatesthe weight α_(m) given to G_(m)(x) in producing the final classifierG(x) (line 3). The individual weights of each of the observations areupdated for the next iteration at line 2d. Observations misclassified byG_(m)(x) have their weights scaled by a factor exp(α_(m)), increasingtheir relative influence for inducing the next classifier G_(m)+1(x) inthe sequence. In some embodiments, modifications of the Freund andSchapire, 1997, Journal of Computer and System Sciences 55, pp. 119-139,boosting methods are used. See, for example, Hasti et al., The Elementsof Statistical Learning, 2001, Springer, New York, Chapter 10, which ishereby incorporated by reference in its entirety. For example, in someembodiments, feature preselection is performed using a technique such asthe nonparametric scoring methods of Park et al., 2002, Pac. Symp.Biocomput. 6, 52-63, which is hereby incorporated by reference in itsentirety. Feature preselection is a form of dimensionality reduction inwhich the genes that discriminate between classifications the best areselected for use in the classifier. Then, the LogitBoost procedureintroduced by Friedman et al., 2000, Ann Stat 28, 337-407 is used ratherthan the boosting procedure of Freund and Schapire. In some embodiments,the boosting and other classification methods of Ben-Dor et al., 2000,Journal of Computational Biology 7, 559-583, hereby incorporated byreference in its entirety, are used in the present invention. In someembodiments, the boosting and other classification methods of Freund andSchapire, 1997, Journal of Computer and System Sciences 55, 119-139,hereby incorporated by reference in its entirety, are used.

In the random subspace method, decision rules are constructed in randomsubspaces of the data feature space. These decision rules are usuallycombined by simple majority voting in the final decision rule. See, forexample, Ho, “The Random subspace method for constructing decisionforests,” IEEE Trans Pattern Analysis and Machine Intelligence, 1998;20(8): 832-844, which is hereby incorporated by reference in itsentirety.

5.5.4 Multiple Additive Regression Trees

Multiple additive regression trees (MART) represents another way toconstruct a decision rule that can be used in the present invention. Ageneric algorithm for MART is:

1. Initialize f0(x) = arg minγ Σ_(i=1) ^(N)L(y_(i), γ). 2. For m = 1 toM: (a) For I =1, 2, . . . , N compute$r_{im} = {- \left\lbrack \frac{\partial{L\left( {y_{i},{f\left( x_{i} \right)}} \right)}}{\partial{f\left( x_{i} \right)}} \right\rbrack_{f = f_{m - 1}}}$(b) Fit a regression tree to the targets rim giving terminal regionsRjm, j = 1, 2, . . . , Jm. (c) For j = 1, 2, . . . , Jm compute${\gamma_{jm} = {a\; r\; g\mspace{11mu} {\min\limits_{\gamma}{\sum\limits_{x_{i} \in R_{jm}}^{\;}\; {{L\left( {y_{i},{{f_{m - 1}\left( x_{i} \right)} + \gamma}} \right)}.}}}}}\;$(d) Update fm(x) = fm − 1(x) + Σ_(j=1) _(Jm) γ_(jm) I(x ∈ R_(jm)) 3.Ouput {circumflex over (f)}(x) = f_(M) (x).

Specific algorithms are obtained by inserting different loss criteriaL(y,f(x)). The first line of the algorithm initializes to the optimalconstant model, which is just a single terminal node tree. Thecomponents of the negative gradient computed in line 2(a) are referredto as generalized pseudo residuals, r. Gradients for commonly used lossfunctions are summarized in Table 10.2, of Hastie et al., 2001, TheElements of Statistical Learning, Springer-Verlag, New York, p. 321,which is hereby incorporated by reference. The algorithm forclassification is similar and is described in Hastie et al., Chapter 10,which is hereby incorporated by reference in its entirety. Tuningparameters associated with the MART procedure are the number ofiterations M and the sizes of each of the constituent trees J_(m), m=1,2, . . . , M.

5.5.5 Decision Rules Derived by Regression

In some embodiments, a decision rule used to classify subjects is builtusing regression. In such embodiments, the decision rule can becharacterized as a regression classifier, preferably a logisticregression classifier. Such a regression classifier includes acoefficient for each of the biomarkers (e.g., a feature for each suchbiomarker) used to construct the classifier. In such embodiments, thecoefficients for the regression classifier are computed using, forexample, a maximum likelihood approach. In such a computation, thefeatures for the biomarkers (e.g., RT-PCR, microarray data) is used. Inparticular embodiments, molecular marker data from only two traitsubgroups is used (e.g., trait subgroup a: will acquire sepsis in adefined time period and trait subgroup b: will not acquire sepsis in adefined time period) and the dependent variable is absence or presenceof a particular trait in the subjects for which biomarker data isavailable.

In another specific embodiment, the training population comprises aplurality of trait subgroups (e.g., three or more trait subgroups, fouror more specific trait subgroups, etc.). These multiple trait subgroupscan correspond to discrete stages in the phenotypic progression fromhealthy, to SIRS, to sepsis, to more advanced stages of sepsis in atraining population. In this specific embodiment, a generalization ofthe logistic regression model that handles multicategory responses canbe used to develop a decision that discriminates between the varioustrait subgroups found in the training population. For example, measureddata for selected molecular markers can be applied to any of themulti-category logit models described in Agresti, An Introduction toCategorical Data Analysis, 1996, John Wiley & Sons, Inc., New York,Chapter 8, hereby incorporated by reference in its entirety, in order todevelop a classifier capable of discriminating between any of aplurality of trait subgroups represented in a training population.

5.5.6 Neural Networks

In some embodiments, the feature data measured for select biomarkers ofthe present invention (e.g., RT-PCR data, mass spectrometry data,microarray data) can be used to train a neural network. A neural networkis a two-stage regression or classification decision rule. A neuralnetwork has a layered structure that includes a layer of input units(and the bias) connected by a layer of weights to a layer of outputunits. For regression, the layer of output units typically includes justone output unit. However, neural networks can handle multiplequantitative responses in a seamless fashion.

In multilayer neural networks, there are input units (input layer),hidden units (hidden layer), and output units (output layer). There is,furthermore, a single bias unit that is connected to each unit otherthan the input units. Neural networks are described in Duda et al.,2001, Pattern Classification, Second Edition, John Wiley & Sons, Inc.,New York; and Hastie et al., 2001, The Elements of Statistical Learning,Springer-Verlag, New York, each of which is hereby incorporated byreference in its entirety. Neural networks are also described inDraghici, 2003, Data Analysis Tools for DNA Microarrays, Chapman &Hall/CRC; and Mount, 2001, Bioinformatics: sequence and genome analysis,Cold Spring Harbor Laboratory Press, Cold Spring Harbor, N.Y., each ofwhich is hereby incorporated by reference in its entirety. What isdisclosed below is some exemplary forms of neural networks.

The basic approach to the use of neural networks is to start with anuntrained network, present a training pattern to the input layer, and topass signals through the net and determine the output at the outputlayer. These outputs are then compared to the target values; anydifference corresponds to an error. This error or criterion function issome scalar function of the weights and is minimized when the networkoutputs match the desired outputs. Thus, the weights are adjusted toreduce this measure of error. For regression, this error can besum-of-squared errors. For classification, this error can be eithersquared error or cross-entropy (deviation). See, e.g., Hastie et al.,2001, The Elements of Statistical Learning, Springer-Verlag, New York,which is hereby incorporated by reference in its entirety.

Three commonly used training protocols are stochastic, batch, andon-line. In stochastic training, patterns are chosen randomly from thetraining set and the network weights are updated for each patternpresentation. Multilayer nonlinear networks trained by gradient descentmethods such as stochastic back-propagation perform a maximum-likelihoodestimation of the weight values in the classifier defined by the networktopology. In batch training, all patterns are presented to the networkbefore learning takes place. Typically, in batch training, severalpasses are made through the training data. In online training, eachpattern is presented once and only once to the net.

In some embodiments, consideration is given to starting values forweights. If the weights are near zero, then the operative part of thesigmoid commonly used in the hidden layer of a neural network (see,e.g., Hastie et al., 2001, The Elements of Statistical Learning,Springer-Verlag, New York, hereby incorporated by reference) is roughlylinear, and hence the neural network collapses into an approximatelylinear classifier. In some embodiments, starting values for weights arechosen to be random values near zero. Hence the classifier starts outnearly linear, and becomes nonlinear as the weights increase. Individualunits localize to directions and introduce nonlinearities where needed.Use of exact zero weights leads to zero derivatives and perfectsymmetry, and the algorithm never moves. Alternatively, starting withlarge weights often leads to poor solutions.

Since the scaling of inputs determines the effective scaling of weightsin the bottom layer, it can have a large effect on the quality of thefinal solution. Thus, in some embodiments, at the outset all expressionvalues are standardized to have mean zero and a standard deviation ofone. This ensures all inputs are treated equally in the regularizationprocess, and allows one to choose a meaningful range for the randomstarting weights. With standardization inputs, it is typical to takerandom uniform weights over the range [−0.7, +0.7].

A recurrent problem in the use of three-layer networks is the optimalnumber of hidden units to use in the network. The number of inputs andoutputs of a three-layer network are determined by the problem to besolved. In the present invention, the number of inputs for a givenneural network will equal the number of biomarkers selected from thetraining population. The number of output for the neural network willtypically be just one. However, in some embodiments more than one outputis used so that more than just two states can be defined by the network.For example, a multi-output neural network can be used to discriminatebetween, healthy phenotypes, various stages of SIRS, and/or variousstages of sepsis. If too many hidden units are used in a neural network,the network will have too many degrees of freedom and is trained toolong, there is a danger that the network will overfit the data. If thereare too few hidden units, the training set cannot be learned. Generallyspeaking, however, it is better to have too many hidden units than toofew. With too few hidden units, the classifier might not have enoughflexibility to capture the nonlinearities in the date; with too manyhidden units, the extra weight can be shrunk towards zero if appropriateregularization or pruning, as described below, is used. In typicalembodiments, the number of hidden units is somewhere in the range of 5to 100, with the number increasing with the number of inputs and numberof training cases.

One general approach to determining the number of hidden units to use isto apply a regularization approach. In the regularization approach, anew criterion function is constructed that depends not only on theclassical training error, but also on classifier complexity.Specifically, the new criterion function penalizes highly complexclassifiers; searching for the minimum in this criterion is to balanceerror on the training set with error on the training set plus aregularization term, which expresses constraints or desirable propertiesof solutions:

J=J _(pat) +λJ _(reg).

The parameter λ is adjusted to impose the regularization more or lessstrongly. In other words, larger values for λ will tend to shrinkweights towards zero: typically cross-validation with a validation setis used to estimate λ. This validation set can be obtained by settingaside a random subset of the training population. Other forms of penaltyhave been proposed, for example the weight elimination penalty (see,e.g., Hastie et al., 2001, The Elements of Statistical Learning,Springer-Verlag, New York, hereby incorporated by reference).

Another approach to determine the number of hidden units to use is toeliminate—prune—weights that are least needed. In one approach, theweights with the smallest magnitude are eliminated (set to zero). Suchmagnitude-based pruning can work, but is nonoptimal; sometimes weightswith small magnitudes are important for learning and training data. Insome embodiments, rather than using a magnitude-based pruning approach,Wald statistics are computed. The fundamental idea in Wald Statistics isthat they can be used to estimate the importance of a hidden unit(weight) in a classifier. Then, hidden units having the least importanceare eliminated (by setting their input and output weights to zero). Twoalgorithms in this regard are the Optimal Brain Damage (OBD) and theOptimal Brain Surgeon (OBS) algorithms that use second-orderapproximation to predict how the training error depends upon a weight,and eliminate the weight that leads to the smallest increase in trainingerror.

Optimal Brain Damage and Optimal Brain Surgeon share the same basicapproach of training a network to local minimum error at weight w, andthen pruning a weight that leads to the smallest increase in thetraining error. The predicted functional increase in the error for achange in full weight vector δw is:

${\delta \; J} = {{{\left( \frac{\partial J}{\partial w} \right)^{t} \cdot \delta}\; w} + {\frac{1}{2}\delta \; {w^{t} \cdot \frac{\partial^{2}J}{\partial w^{2}} \cdot \delta}\; w} + {O\left( {{\delta \; w}}^{3} \right)}}$

where

$\frac{\partial^{2}J}{\partial w^{2}}$

is the Hessian matrix. The first term vanishes at a local minimum inerror; third and higher order terms are ignored. The general solutionfor minimizing this function given the constraint of deleting one weightis:

${\delta \; w} = {{{- \frac{w_{q}}{\left\lbrack H^{- 1} \right\rbrack_{qq}}}{H^{- 1} \cdot u_{q}}\mspace{14mu} {and}\mspace{14mu} L_{q}} = {\frac{1}{2} - \frac{w_{q}^{2}}{\left\lbrack H^{- 1} \right\rbrack_{qq}}}}$

Here, u_(q) is the unit vector along the qth direction in weight spaceand L_(q) is approximation to the saliency of the weight q—the increasein training error if weight q is pruned and the other weights updatedδw. These equations require the inverse of H. One method to calculatethis inverse matrix is to start with a small value, H₀ ⁻¹=α⁻¹I, where αis a small parameter—effectively a weight constant. Next the matrix isupdated with each pattern according to

$\begin{matrix}{H_{m + 1}^{- 1} = {H_{m}^{- 1} - \frac{H_{m}^{- 1}X_{m + 1}X_{m + 1}^{T}H_{m}^{- 1}}{\frac{n}{a_{m}} + {X_{m + 1}^{T}H_{m}^{- 1}X_{m + 1}}}}} & {{Eqn}.\; 1}\end{matrix}$

where the subscripts correspond to the pattern being presented and α_(m)decreases with m. After the full training set has been presented, theinverse Hessian matrix is given by H⁻¹=H_(n) ⁻¹. In algorithmic form,the Optimal Brain Surgeon method is:

begin initialize n_(H), w, θ   train a reasonably large network tominimum error   do compute H⁻¹ by Eqn. 1    $\left. q^{*}\leftarrow{\arg \underset{q}{{\; \;}\min}\mspace{11mu} {w_{q}^{2}/\left( {2\left\lfloor H^{- 1} \right\rfloor_{qq}} \right)}\mspace{11mu} \left( {{saliency}\mspace{14mu} L_{q}} \right)} \right.$   $\left. w\leftarrow{w - {\frac{w_{q}^{*}}{\left\lbrack H^{- 1} \right\rbrack_{q^{*}q^{*}}}H^{- 1}e_{q^{*}}\mspace{11mu} \left( {{saliency}\mspace{14mu} L_{q}} \right)}} \right.$  until J(w) > θ  return w end

The Optimal Brain Damage method is computationally simpler because thecalculation of the inverse Hessian matrix in line 3 is particularlysimple for a diagonal matrix. The above algorithm terminates when theerror is greater than a criterion initialized to be θ. Another approachis to change line 6 to terminate when the change in J(w) due toelimination of a weight is greater than some criterion value. In someembodiments, the back-propagation neural network See, for example Abdi,1994, “A neural network primer,” J. Biol System. 2, 247-283, herebyincorporated by reference in its entirety.

5.5.7 Clustering

In some embodiments, features for select biomarkers of the presentinvention are used to cluster a training set. For example, consider thecase in which ten features (corresponding to ten biomarkers) describedin the present invention is used. Each member m of the trainingpopulation will have feature values (e.g. expression values) for each ofthe ten biomarkers. Such values from a member m in the trainingpopulation define the vector:

x_(1m) X_(2m) X_(3m) X_(4m) X_(5m) X_(6m) X_(7m) X_(8m) X_(9m) X_(10m)

where X_(im) is the expression level of the i^(th) biomarker in organismm. If there are m organisms in the training set, selection of ibiomarkers will define m vectors. Note that the methods of the presentinvention do not require that each the expression value of every singlebiomarker used in the vectors be represented in every single vector m.In other words, data from a subject in which one of the i^(th)biomarkers is not found can still be used for clustering. In suchinstances, the missing expression value is assigned either a “zero” orsome other normalized value. In some embodiments, prior to clustering,the feature values are normalized to have a mean value of zero and unitvariance.

Those members of the training population that exhibit similar expressionpatterns across the training group will tend to cluster together. Aparticular combination of genes of the present invention is consideredto be a good classifier in this aspect of the invention when the vectorscluster into the trait groups found in the training population. Forinstance, if the training population includes class a: subjects that donot develop sepsis, and class b: subjects that develop sepsis, an idealclustering classifier will cluster the population into two groups, withone cluster group uniquely representing class a and the other clustergroup uniquely representing class b.

Clustering is described on pages 211-256 of Duda and Hart, PatternClassification and Scene Analysis, 1973, John Wiley & Sons, Inc., NewYork, (hereinafter “Duda 1973”) which is hereby incorporated byreference in its entirety. As described in Section 6.7 of Duda 1973, theclustering problem is described as one of finding natural groupings in adataset. To identify natural groupings, two issues are addressed. First,a way to measure similarity (or dissimilarity) between two samples isdetermined. This metric (similarity measure) is used to ensure that thesamples in one cluster are more like one another than they are tosamples in other clusters. Second, a mechanism for partitioning the datainto clusters using the similarity measure is determined.

Similarity measures are discussed in Section 6.7 of Duda 1973, where itis stated that one way to begin a clustering investigation is to definea distance function and to compute the matrix of distances between allpairs of samples in a dataset. If distance is a good measure ofsimilarity, then the distance between samples in the same cluster willbe significantly less than the distance between samples in differentclusters. However, as stated on page 215 of Duda 1973, clustering doesnot require the use of a distance metric. For example, a nonmetricsimilarity function s(x, x′) can be used to compare two vectors x andx′. Conventionally, s(x, x′) is a symmetric function whose value islarge when x and x′ are somehow “similar”. An example of a nonmetricsimilarity function s(x, x′) is provided on page 216 of Duda 1973.

Once a method for measuring “similarity” or “dissimilarity” betweenpoints in a dataset has been selected, clustering requires a criterionfunction that measures the clustering quality of any partition of thedata. Partitions of the data set that extremize the criterion functionare used to cluster the data. See page 217 of Duda 1973. Criterionfunctions are discussed in Section 6.8 of Duda 1973.

More recently, Duda et al., Pattern Classification, 2^(nd) edition, JohnWiley & Sons, Inc. New York, has been published. Pages 537-563 describeclustering in detail. More information on clustering techniques can befound in Kaufman and Rousseeuw, 1990, Finding Groups in Data: AnIntroduction to Cluster Analysis, Wiley, New York, N.Y.; Everitt, 1993,Cluster analysis (3d ed.), Wiley, New York, N.Y.; and Backer, 1995,Computer-Assisted Reasoning in Cluster Analysis, Prentice Hall, UpperSaddle River, N.J. Particular exemplary clustering techniques that canbe used in the present invention include, but are not limited to,hierarchical clustering (agglomerative clustering using nearest-neighboralgorithm, farthest-neighbor algorithm, the average linkage algorithm,the centroid algorithm, or the sum-of-squares algorithm), k-meansclustering, fuzzy k-means clustering algorithm, and Jarvis-Patrickclustering.

5.5.8 Principle Component Analysis

Principal component analysis (PCA) has been proposed to analyze geneexpression data. More generally, PCA can be used to analyze featurevalue data of biomarkers of the present invention in order to constructa decision rule that discriminates converters from nonconverters.Principal component analysis is a classical technique to reduce thedimensionality of a data set by transforming the data to a new set ofvariable (principal components) that summarize the features of the data.See, for example, Jolliffe, 1986, Principal Component Analysis,Springer, New York, which is hereby incorporated by reference. Principalcomponent analysis is also described in Draghici, 2003, Data AnalysisTools for DNA Microarrays, Chapman & Hall/CRC, which is herebyincorporated by reference. What follows is non-limiting examples ofprincipal components analysis.

Principal components (PCs) are uncorrelated and are ordered such thatthe k^(th) PC has the kth largest variance among PCs. The k^(th) PC canbe interpreted as the direction that maximizes the variation of theprojections of the data points such that it is orthogonal to the firstk−1 PCs. The first few PCs capture most of the variation in the dataset. In contrast, the last few PCs are often assumed to capture only theresidual ‘noise’ in the data.

PCA can also be used to create a classifier in accordance with thepresent invention. In such an approach, vectors for the selectbiomarkers of the present invention can be constructed in the samemanner described for clustering above. In fact, the set of vectors,where each vector represents the feature values (e.g., abundance values)for the select genes from a particular member of the trainingpopulation, can be viewed as a matrix. In some embodiments, this matrixis represented in a Free-Wilson method of qualitative binary descriptionof monomers (Kubinyi, 1990, 3D QSAR in drug design theory methods andapplications, Pergamon Press, Oxford, pp 589-638), and distributed in amaximally compressed space using PCA so that the first principalcomponent (PC) captures the largest amount of variance informationpossible, the second principal component (PC) captures the secondlargest amount of all variance information, and so forth until allvariance information in the matrix has been considered.

Then, each of the vectors (where each vector represents a member of thetraining population) is plotted. Many different types of plots arepossible. In some embodiments, a one-dimensional plot is made. In thisone-dimensional plot, the value for the first principal component fromeach of the members of the training population is plotted. In this formof plot, the expectation is that members of a first subgroup (e.g. thosesubjects that do not develop sepsis in a determined time period) willcluster in one range of first principal component values and members ofa second subgroup (e.g., those subjects that develop sepsis in adetermined time period) will cluster in a second range of firstprincipal component values.

In one ideal example, the training population comprises two subgroups:“sepsis” and “SIRS.” The first principal component is computed using themolecular marker expression values for the select biomarkers of thepresent invention across the entire training population data set. Then,each member of the training set is plotted as a function of the valuefor the first principal component. In this ideal example, those membersof the training population in which the first principal component ispositive are the “responders” and those members of the trainingpopulation in which the first principal component is negative are“subjects with sepsis.”

In some embodiments, the members of the training population are plottedagainst more than one principal component. For example, in someembodiments, the members of the training population are plotted on atwo-dimensional plot in which the first dimension is the first principalcomponent and the second dimension is the second principal component. Insuch a two-dimensional plot, the expectation is that members of eachsubgroup represented in the training population will cluster intodiscrete groups. For example, a first cluster of members in thetwo-dimensional plot will represent subjects that develop sepsis in agiven time period and a second cluster of members in the two-dimensionalplot will represent subjects that do not develop sepsis in a given timeperiod.

5.5.9 Nearest Neighbor Analysis

Nearest neighbor classifiers are memory-based and require no classifierto be fit. Given a query point x₀, the k training points x_((r)), r, . .. , k closest in distance to x₀ are identified and then the point x₀ isclassified using the k nearest neighbors. Ties can be broken at random.In some embodiments, Euclidean distance in feature space is used todetermine distance as:

d _((i)) =∥x _((i)) —x _(o)∥.

Typically, when the nearest neighbor algorithm is used, the expressiondata used to compute the linear discriminant is standardized to havemean zero and variance 1. In the present invention, the members of thetraining population are randomly divided into a training set and a testset. For example, in one embodiment, two thirds of the members of thetraining population are placed in the training set and one third of themembers of the training population are placed in the test set. A selectcombination of biomarkers of the present invention represents thefeature space into which members of the test set are plotted. Next, theability of the training set to correctly characterize the members of thetest set is computed. In some embodiments, nearest neighbor computationis performed several times for a given combination of biomarkers of thepresent invention. In each iteration of the computation, the members ofthe training population are randomly assigned to the training set andthe test set. Then, the quality of the combination of biomarkers istaken as the average of each such iteration of the nearest neighborcomputation.

The nearest neighbor rule can be refined to deal with issues of unequalclass priors, differential misclassification costs, and featureselection. Many of these refinements involve some form of weightedvoting for the neighbors. For more information on nearest neighboranalysis, see Duda, Pattern Classification, Second Edition, 2001, JohnWiley & Sons, Inc; and Hastie, 2001, The Elements of StatisticalLearning, Springer, New York, each of which is hereby incorporated byreference in its entirety.

5.5.10 Linear Discriminant Analysis

Linear discriminant analysis (LDA) attempts to classify a subject intoone of two categories based on certain object properties. In otherwords, LDA tests whether object attributes measured in an experimentpredict categorization of the objects. LDA typically requires continuousindependent variables and a dichotomous categorical dependent variable.In the present invention, the feature values for the select combinationsof biomarkers of the present invention across a subset of the trainingpopulation serve as the requisite continuous independent variables. Thetrait subgroup classification of each of the members of the trainingpopulation serves as the dichotomous categorical dependent variable.

LDA seeks the linear combination of variables that maximizes the ratioof between-group variance and within-group variance by using thegrouping information. Implicitly, the linear weights used by LDA dependon how the feature values of a molecular marker across the training setseparates in the two groups (e.g., a group a that develops sepsis duringa defined time period and a group b that does not develop sepsis duringa defined time period) and how these feature values correlate with thefeature values of other biomarkers. In some embodiments, LDA is appliedto the data matrix of the N members in the training sample by Kbiomarkers in a combination of biomarkers described in the presentinvention. Then, the linear discriminant of each member of the trainingpopulation is plotted. Ideally, those members of the training populationrepresenting a first subgroup (e.g. those subjects that develop sepsisin a defined time period) will cluster into one range of lineardiscriminant values (e.g., negative) and those member of the trainingpopulation representing a second subgroup (e.g. those subjects that willnot develop sepsis in a defined time period) will cluster into a secondrange of linear discriminant values (e.g., positive). The LDA isconsidered more successful when the separation between the clusters ofdiscriminant values is larger. For more information on lineardiscriminant analysis, see Duda, Pattern Classification, Second Edition,2001, John Wiley & Sons, Inc; and Hastie, 2001, The Elements ofStatistical Learning, Springer, New York; and Venables & Ripley, 1997,Modern Applied Statistics with s-plus, Springer, New York, each of whichis hereby incorporated by reference in its entirety.

5.5.11 Quadratic Discriminant Analysis

Quadratic discriminant analysis (QDA) takes the same input parametersand returns the same results as LDA. QDA uses quadratic equations,rather than linear equations, to produce results. LDA and QDA areinterchangeable, and which to use is a matter of preference and/oravailability of software to support the analysis. Logistic regressiontakes the same input parameters and returns the same results as LDA andQDA.

5.5.12 Support Vector Machines

In some embodiments of the present invention, support vector machines(SVMs) are used to classify subjects using feature values of the genesdescribed in the present invention. SVMs are a relatively new type oflearning algorithm. See, for example, Cristianini and Shawe-Taylor,2000, An Introduction to Support Vector Machines, Cambridge UniversityPress, Cambridge; Boser et al., 1992, “A training algorithm for optimalmargin classifiers,” in Proceedings of the 5^(th) Annual ACM Workshop onComputational Learning Theory, ACM Press, Pittsburgh, Pa., pp. 142-152;Vapnik, 1998, Statistical Learning Theory, Wiley, New York; Mount, 2001,Bioinformatics: sequence and genome analysis, Cold Spring HarborLaboratory Press, Cold Spring Harbor, N.Y., Duda, PatternClassification, Second Edition, 2001, John Wiley & Sons, Inc.; andHastie, 2001, The Elements of Statistical Learning, Springer, New York;and Furey et al., 2000, Bioinformatics 16, 906-914, each of which ishereby incorporated by reference in its entirety. When used forclassification, SVMs separate a given set of binary labeled datatraining data with a hyper-plane that is maximally distance from them.For cases in which no linear separation is possible, SVMs can work incombination with the technique of ‘kernels’, which automaticallyrealizes a non-linear mapping to a feature space. The hyper-plane foundby the SVM in feature space corresponds to a non-linear decisionboundary in the input space.

In one approach, when a SVM is used, the feature data is standardized tohave mean zero and unit variance and the members of a trainingpopulation are randomly divided into a training set and a test set. Forexample, in one embodiment, two thirds of the members of the trainingpopulation are placed in the training set and one third of the membersof the training population are placed in the test set. The expressionvalues for a combination of genes described in the present invention isused to train the SVM. Then the ability for the trained SVM to correctlyclassify members in the test set is determined. In some embodiments,this computation is performed several times for a given combination ofmolecular markers. In each iteration of the computation, the members ofthe training population are randomly assigned to the training set andthe test set. Then, the quality of the combination of biomarkers istaken as the average of each such iteration of the SVM computation.

5.5.13 Evolutionary Methods

Inspired by the process of biological evolution, evolutionary methods ofdecision rule design employ a stochastic search for an decision rule. Inbroad overview, such methods create several decision rules—apopulation—from a combination of biomarkers described in the presentinvention. Each decision rule varies somewhat from the other. Next, thedecision rules are scored on feature data across the trainingpopulation. In keeping with the analogy with biological evolution, theresulting (scalar) score is sometimes called the fitness. The decisionrules are ranked according to their score and the best decision rulesare retained (some portion of the total population of decision rules).Again, in keeping with biological terminology, this is called survivalof the fittest. The decision rules are stochastically altered in thenext generation—the children or offspring. Some offspring decision ruleswill have higher scores than their parent in the previous generation,some will have lower scores. The overall process is then repeated forthe subsequent generation: the decision rules are scored and the bestones are retained, randomly altered to give yet another generation, andso on. In part, because of the ranking, each generation has, on average,a slightly higher score than the previous one. The process is haltedwhen the single best decision rule in a generation has a score thatexceeds a desired criterion value. More information on evolutionarymethods is found in, for example, Duda, Pattern Classification, SecondEdition, 2001, John Wiley & Sons, Inc.

5.5.14 Other Data Analysis Algorithms

The data analysis algorithms described above are merely examples of thetypes of methods that can be used to construct a decision rule fordiscriminating converters from nonconverters. Moreover, combinations ofthe techniques described above can be used. Some combinations, such asthe use of the combination of decision trees and boosting, have beendescribed. However, many other combinations are possible. In addition,in other techniques in the art such as Projection Pursuit and WeightedVoting can be used to construct decision rules.

5.6 Biomarkers

In specific embodiments, the present invention provides biomarkers thatare useful in diagnosing or predicting sepsis and/or its stages ofprogression in a subject. While the methods of the present invention mayuse an unbiased approach to identifying predictive biomarkers, it willbe clear to the artisan that specific groups of biomarkers associatedwith physiological responses or with various signaling pathways may bethe subject of particular attention. This is particularly the case wherebiomarkers from a biological sample are contacted with an array that canbe used to measure the amount of various biomarkers through direct andspecific interaction with the biomarkers (e.g., an antibody array or anucleic acid array). In this case, the choice of the components of thearray may be based on a suggestion that a particular pathway is relevantto the determination of the status of sepsis or SIRS in a subject. Theindication that a particular biomarker has a feature that is predictiveor diagnostic of sepsis or SIRS may give rise to an expectation thatother biomarkers that are physiologically regulated in a concertedfashion likewise may provide a predictive or diagnostic feature. Theartisan will appreciate, however, that such an expectation may not berealized because of the complexity of biological systems. For example,if the amount of a specific mRNA biomarker were a predictive feature, aconcerted change in mRNA expression of another biomarker might not bemeasurable, if the expression of the other biomarker was regulated at apost-translational level. Further, the mRNA expression level of abiomarker may be affected by multiple converging pathways that may ormay not be involved in a physiological response to sepsis.

Biomarkers can be obtained from any biological sample, which can be, byway of example and not of limitation, whole blood, plasma, saliva,serum, red blood cells, platelets, neutrophils, eosinophils, basophils,lymphocytes, monocytes, urine, cerebral spinal fluid, sputum, stool,cells and cellular extracts, or other biological fluid sample, tissuesample or tissue biopsy from a host or subject. The precise biologicalsample that is taken from the subject may vary, but the samplingpreferably is minimally invasive and is easily performed by conventionaltechniques.

Measurement of a phenotypic change may be carried out by anyconventional technique. Measurement of body temperature, respirationrate, pulse, blood pressure, or other physiological parameters can beachieved via clinical observation and measurement. Measurements ofbiomarker molecules may include, for example, measurements that indicatethe presence, concentration, expression level, or any other valueassociated with a biomarker molecule. The form of detection of biomarkermolecules typically depends on the method used to form a profile ofthese biomarkers from a biological sample. See Section 5.4, above, andTables 30, I, J, K, L, and M below.

In a particular embodiment, the biomarker profile comprises at least twodifferent biomarkers listed in column four or five of Table 30. Thebiomarker profile further comprises a respective corresponding featurefor the at least two biomarkers. Such biomarkers can be, for example,mRNA transcripts, cDNA or some other nucleic acid, for example amplifiednucleic acid, or proteins. Generally, the at least two biomarkers arederived from at least two different genes. In the case where a biomarkerin the at least two different biomarkers is listed in column four ofTable 30, the biomarker can be, for example, a transcript made by thelisted gene, a complement thereof, or a discriminating fragment orcomplement thereof, or a cDNA thereof, or a discriminating fragment ofthe cDNA, or a discriminating amplified nucleic acid moleculecorresponding to all or a portion of the transcript or its complement,or a protein encoded by the gene, or a discriminating fragment of theprotein, or an indication of any of the above. Further still, thebiomarker can be, for example, a protein listed in column five of Table30, or a discriminating fragment of the protein, or an indication of anyof the above. Here, a discriminating molecule or fragment is a moleculeor fragment that, when detected, indicates presence or abundance of theabove-identified transcript, cDNA, amplified nucleic acid, or protein.In accordance with this embodiment, the biomarker profiles of thepresent invention can be obtained using any standard assay known tothose skilled in the art, or in an assay described herein, to detect abiomarker. Such assays are capable, for example, of detecting theproducts of expression (e.g., nucleic acids and/or proteins) of aparticular gene or allele of a gene of interest (e.g., a gene disclosedin Table 30). In one embodiment, such an assay utilizes a nucleic acidmicroarray.

In a particular embodiment, the biomarker profile comprises at least twodifferent biomarkers that each contain one of the probesets listed incolumn 2 of Table 30, biomarkers that contain the complement of one ofthe probesets of Table 30, or biomarkers that contain an amino acidsequence encoded by a gene that either contains one of the probesets ofTable 30 or the complement of one of the probesets of Table 30. Suchbiomarkers can be, for example, mRNA transcripts, cDNA or some othernucleic acid, for example amplified nucleic acid, or proteins. Thebiomarker profile further comprises a respective corresponding featurefor the at least two biomarkers. Generally, the at least two biomarkersare derived from at least two different genes. In the case where abiomarker is based upon a gene that includes the sequence of a probesetlisted in Table 30, the biomarker can be, for example, a transcript madeby the gene, a complement thereof, or a discriminating fragment orcomplement thereof, or a cDNA thereof, or a discriminating fragment ofthe cDNA, or a discriminating amplified nucleic acid moleculecorresponding to all or a portion of the transcript or its complement,or a protein encoded by the gene, or a discriminating fragment of theprotein, or an indication of any of the above. Further still, thebiomarker can be, for example, a protein encoded by a gene that includesa probeset sequence described in Table 30, or a discriminating fragmentof the protein, or an indication of any of the above. Here, adiscriminating molecule or fragment is a molecule or fragment that, whendetected, indicates presence or abundance of the above-identifiedtranscript, cDNA, amplified nucleic acid, or protein.

In some embodiments the biomarker profile has between 2 and 626biomarkers listed in Table 30. In some embodiments, the biomarkerprofile has between 3 and 50 biomarkers listed in Table 30. In someembodiments, the biomarker profile has between 4 and 25 biomarkerslisted in Table 30. In some embodiments, the biomarker profile has atleast 3 biomarkers listed in Table 30. In some embodiments, thebiomarker profile has at least 4 biomarkers listed in Table 30. In someembodiments, the biomarker profile has at least 2, 3, 4, 5, 6, 7, 8, 9,10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55,60, 65, 70, 75, 80, 85, 90, 95, 96, or 100 biomarkers listed in Table30. In some embodiments, each such biomarker is a nucleic acid. In someembodiments, each such biomarker is a protein.

In some embodiments, some of the biomarkers in the biomarker profile arenucleic acids and some of the biomarkers in the biomarker profile areproteins. In some embodiments the biomarker profile has between 2 and130 biomarkers listed in Table 31. In some embodiments, the biomarkerprofile has between 3 and 50 biomarkers listed in Table 31. In someembodiments, the biomarker profile has between 4 and 25 biomarkerslisted in Table 31. In some embodiments, the biomarker profile has atleast 3 biomarkers listed in Table 31. In some embodiments, thebiomarker profile has at least 4 biomarkers listed in Table 30. In someembodiments, the biomarker profile has at least 6, 10, 15, 20, 25, 30,35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 96, or 100biomarkers listed in Table 31.

In some embodiments the biomarker profile has between 2 and 10biomarkers listed in Table 33. In some embodiments, the biomarkerprofile has between 3 and 10 biomarkers listed in Table 32. In someembodiments, the biomarker profile has between 4 and 10 biomarkerslisted in Table 32. In some embodiments, the biomarker profile has atleast 3 biomarkers listed in Table 32. In some embodiments, thebiomarker profile has at least 4 biomarkers listed in Table 32. In someembodiments, the biomarker profile has at least 6, 7, 8, 9, or 10biomarkers listed in Table 32. In some embodiments, each such biomarkeris a nucleic acid. In some embodiments, each such biomarker is aprotein. In some embodiments, some of the biomarkers in the biomarkerprofile are nucleic acids and some of the biomarkers in the biomarkerprofile are proteins.

In some embodiments the biomarker profile has between 2 and 10biomarkers listed in Table 33. In some embodiments, the biomarkerprofile has between 3 and 10 biomarkers listed in Table 33. In someembodiments, the biomarker profile has between 4 and 10 biomarkerslisted in Table 33. In some embodiments, the biomarker profile has atleast 3 biomarkers listed in Table 33. In some embodiments, thebiomarker profile has at least 4 biomarkers listed in Table 33. In someembodiments, the biomarker profile has at least 6, 7, 8, 9, or 10biomarkers listed in Table 33. In some embodiments, each such biomarkeris a nucleic acid. In some embodiments, each such biomarker is aprotein. In some embodiments, some of the biomarkers in the biomarkerprofile are nucleic acids and some of the biomarkers in the biomarkerprofile are proteins.

In some embodiments the biomarker profile has between 2 and 130biomarkers listed in Table 34. In some embodiments, the biomarkerprofile has between 3 and 40 biomarkers listed in Table 34. In someembodiments, the biomarker profile has between 4 and 25 biomarkerslisted in Table 34. In some embodiments, the biomarker profile has atleast 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,25, 30, 35, or 40 biomarkers listed in Table 34. In some embodiments,each such biomarker is a nucleic acid. In some embodiments, each suchbiomarker is a protein. In some embodiments, some of the biomarkers inthe biomarker profile are nucleic acids and some of the biomarkers inthe biomarker profile are proteins.

In some embodiments the biomarker profile has between 2 and 7 biomarkerslisted in Table 36. In some embodiments, the biomarker profile hasbetween 3 and 6 biomarkers listed in Table 36. In some embodiments, thebiomarker profile has between 4 and 7 biomarkers listed in Table 36. Insome embodiments, the biomarker profile has at least 3 biomarkers listedin Table 36. In some embodiments, the biomarker profile has at least 4biomarkers listed in Table 36. In some embodiments, the biomarkerprofile has at least 6, 7, 8, 9, or 10 biomarkers listed in Table 36. Insome embodiments, each such biomarker is a nucleic acid. In someembodiments, each such biomarker is a protein. In some embodiments, someof the biomarkers in the biomarker profile are nucleic acids and some ofthe biomarkers in the biomarker profile are proteins.

In some embodiments the biomarker profile has between 2 and 53biomarkers listed in Table I. In some embodiments, the biomarker profilehas between 3 and 50 biomarkers listed in Table I. In some embodiments,the biomarker profile has between 4 and 25 biomarkers listed in Table I.In some embodiments, the biomarker profile has at least 3 biomarkerslisted in Table I. In some embodiments, the biomarker profile has atleast 4 biomarkers listed in Table I. In some embodiments, the biomarkerprofile has at least 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 15, 17, 18,19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36,37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, or 53biomarkers listed in Table I. In some embodiments, each of thebiomarkers in the biomarker profile is a nucleic acid in Table I. Insome embodiments, each of the biomarkers in the biomarker profile is aprotein in Table I. In some embodiments, some of the biomarkers in abiomarker profile are proteins in Table I and some of the biomarkers inthe same biomarker profile are nucleic acids in Table I.

In some embodiments the biomarker profile has between 2 and 44biomarkers listed in Table J. In some embodiments, the biomarker profilehas between 3 and 44 biomarkers listed in Table J. In some embodiments,the biomarker profile has between 4 and 25 biomarkers listed in Table J.In some embodiments, the biomarker profile has at least 3 biomarkerslisted in Table J. In some embodiments, the biomarker profile has atleast 4 biomarkers listed in Table J. In some embodiments, the biomarkerprofile has at least 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 15, 17, 18,19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36,37, 38, 39, 40, 41, 42, or 43 biomarkers listed in Table J. In someembodiments, each of the biomarkers in the biomarker profile is anucleic acid in Table J. In some embodiments, each of the biomarkers inthe biomarker profile is a protein in Table J. In some embodiments, someof the biomarkers in a biomarker profile are proteins in Table J andsome of the biomarkers in the same biomarker profile are nucleic acidsin Table J.

In some embodiments the biomarker profile has between 2 and 10biomarkers listed in Table K. In some embodiments, the biomarker profilehas between 3 and 10 biomarkers listed in Table K. In some embodiments,the biomarker profile has between 4 and 10 biomarkers listed in Table K.In some embodiments, the biomarker profile has at least 3 biomarkerslisted in Table K. In some embodiments, the biomarker profile has atleast 4 biomarkers listed in Table K. In some embodiments, the biomarkerprofile has at least 5, 6, 7, 8, or 9 biomarkers listed in Table K. Insome embodiments, each of the biomarkers in the biomarker profile is anucleic acid in Table K. In some embodiments, each of the biomarkers inthe biomarker profile is a protein in Table K. In some embodiments, someof the biomarkers in a biomarker profile are proteins in Table K andsome of the biomarkers in the same biomarker profile are nucleic acidsin Table K.

5.6.1 Isolation of Useful Biomarkers

The biomarkers of the present invention may, for example, be used toraise antibodies that bind the biomarker if it is a protein (usingmethods described in Section 5.4.2, supra, or any method well known tothose of skill in the art), or they may be used to develop a specificoligonucleotide probe, if it is a nucleic acid, for example, using amethod described in Section 5.4.1, supra, or any method well known tothose of skill in the art. The skilled artisan will readily appreciatethat useful features can be further characterized to determine themolecular structure of the biomarker. Methods for characterizingbiomarkers in this fashion are well-known in the art and include X-raycrystallography, high-resolution mass spectrometry, infraredspectrometry, ultraviolet spectrometry and nuclear magnetic resonance.Methods for determining the nucleotide sequence of nucleic acidbiomarkers, the amino acid sequence of polypeptide biomarkers, and thecomposition and sequence of carbohydrate biomarkers also are well-knownin the art.

5.7 Application of the Present Invention to Sirs Subjects

In one embodiment, the presently described methods are used to screenSIRS subjects who are at risk for developing sepsis. A biological sampleis taken from a SIRS-positive subject and used to construct a biomarkerprofile. The biomarker profile is then evaluated to determine whetherthe feature values of the biomarker profile satisfy a first value setassociated with a particular decision rule. This evaluation classifiesthe subject as a converter or a nonconverter. A treatment regimen maythen be initiated to forestall or prevent the progression of sepsis whenthe subject is classified as a converter.

5.8 Application of the Present Invention to Stages of Sepsis

In one embodiment, the presently described methods are used to screensubjects who are particularly at risk for developing a certain stage ofsepsis. A biological sample is taken from a subject and used toconstruct a biomarker profile. The biomarker profile is then evaluatedto determine whether the feature values of the biomarker profile satisfya first value set associated with a particular decision rule. Thisevaluation classifies the subject as having or not having a particularstage of sepsis. A treatment regimen may then be initiated to treat thespecific stage of sepsis. In some embodiments, the stage of sepsis isfor example, onset of sepsis, severe sepsis, septic shock, or multipleorgan dysfunction.

5.9 Exemplary Embodiments

In some embodiments of the present invention, a biomarker profile isobtained using a biological sample from a test subject, particularly asubject at risk of developing sepsis, having sepsis, or suspected ofhaving sepsis. The biomarker profile in such embodiments is evaluated.This evaluation can be made, for example, by applying a decision rule tothe test subject. The decision rule can, for example, be or have beenconstructed based upon the biomarker profiles obtained from subjects inthe training population. The training population, in one embodiment,includes (a) subjects that had SIRS and were then diagnosed as septicduring an observation time period as well as (b) subjects that had SIRSand were not diagnosed as septic during an observation time period. Ifthe biomarker profile from the test subject contains appropriatelycharacteristic features, then the test subject is diagnosed as having amore likely chance of becoming septic, as being afflicted with sepsis oras being at the particular stage in the progression of sepsis. Variouspopulations of subjects including those who are suffering from SIRS(e.g., SIRS-positive subjects) or those who are suffering from aninfection but who are not suffering from SIRS (e.g., SIRS-negativesubjects) can serve as training populations. Accordingly, the presentinvention allows the clinician to distinguish, inter alia, between thosesubjects who do not have SIRS, those who have SIRS but are not likely todevelop sepsis within a given time frame, those who have SIRS and whoare at risk of eventually becoming septic, and those who are sufferingfrom a particular stage in the progression of sepsis. For more detailson suitable training populations and suitable data collected from suchpopulations, see Section 5.5, above.

5.10 Use of Annotation Data to Identify Discriminating Biomarkers

In some embodiments, data analysis algorithms identify a large set ofbiomarkers whose features discriminate between converters andnonconverters. For example, in some embodiments, application of a dataanalysis algorithm to a training population results in the selection ofmore than 500 biomarkers, more than 1000 biomarkers, or more than 10,000biomarkers. In some embodiments, further reduction in the number ofbiomarkers that are deemed to be discriminating is desired. Accordingly,in some embodiments, filtering rules that are complementary to dataanalysis algorithms (e.g., the data analysis algorithms of Section 5.5)are used to further reduce the list of discriminating biomarkersidentified by the data analysis algorithms. Specifically, the list ofbiomarkers identified by application of one or more data analysisalgorithms to the biomarker profile data measured in a trainingpopulation is further refined by application of annotation data basedfiltering rules to the list. In such embodiments, those biomarkers inthe set of biomarkers identified by the one or more data analysisalgorithms that satisfy the one or more applied annotation data basedfiltering rules remain in the set of discriminating biomarkers. In someinstances, those biomarkers in the set of biomarkers identified by theone or more data analysis algorithms that do not satisfy the one or moreapplied annotation data based filtering rules are removed from the set.In other instances, those biomarkers in the set of biomarkers identifiedby the one or more data analysis algorithms that do not satisfy the oneor more applied annotation data based filtering rules stay in the setand those that satisfy the one or more applied annotation data basedfiltering rules are removed from the set. In this way, annotation datacan be used to reduce the number of biomarkers in the set ofdiscriminating biomarkers identified by the data analysis algorithms.

Annotation data based filtering rules are rules based upon annotationdata. Annotation data refers to any type of data that describes aproperty of a biomarker. An example of annotation data is theidentification of biological pathways to which a given biomarkerbelongs. Another example of annotation data is enzymatic class (e.g.,phosphodiesterases, kinases, metalloproteinases, etc.). Still otherexamples of annotation data include, but are not limited to, proteindomain information, enzymatic substrate information, enzymatic reactioninformation, and protein interaction data. Yet another example ofannotation data is disease association, in other words, which diseaseprocess a given biomarker has been linked to or otherwise affects.Another form of annotation data is any type of data that associatesbiomarker expression, other forms of biomarker abundance, and/orbiomarker activity, with cellular localization, tissue typelocalization, and/or cell type localization.

As the name implies, annotation data is used to construct an annotationdata based filtering rule. An example of an annotation data basedfiltering rule is:

Annotation Rule 1:

remove all transcription factors from the training set.

Application of this filtering rule to a set of biomarkers will removeall transcription factors from the set.

Another type of annotation data based filtering rule is:

Annotation rule 2:

keep all biomarkers that are enriched for annotation X in a biomarkerlist.

Application of this filtering rule will only keep those biomarkers in agiven list that are enriched (overrepresented) for annotation X in thelist. To more fully appreciate this filtering rule, consider anexemplary biomarker set that has been identified by application of adata analysis algorithm (Section 5.5) to biomarker profiles measuredusing training population data measured in accordance with a techniquedisclosed in Section 5.4. This exemplary biomarker set has 500biomarkers. Assume, for in this illustrative example, that the full setof biomarkers in a human consists of 25,000 biomarkers. Here, the 25,000biomarkers is a population and the 500 biomarker set is the sample. Asused here, the term “population” consists of all possible observablebiomarkers. The term “sample” is the data that is actually considered.Now, for this example, let X=kinases. Suppose there are 800 known humankinases and further suppose that the set of 500 biomarkers was randomlyselected with respect to kinases. Under these circumstances, the list of500 biomarkers identified by the data analysis algorithms should selectabout (500/25,000)*800=16 kinases. Since there are, in fact, 50 kinasesin the sample, a conclusion can be reached that kinases are indeedenriched in the sample relative to the population.

More formally, in this example, a determination can be made as towhether kinases are enriched in the set of biomarkers identified by thedata analysis algorithm (the sample) relative to the population byanalysis of the two-way contingency table that describes the observedsample and population:

Kinase Group Yes No Total Population 800 24,200 25,000 Sample 50 450 500

Following Agresti, 1996, An Introduction to Categorical Data Analysis,John Wiley & Sons, New York, which is hereby incorporated by referencein its entirety, this two-way contingency table can be analyzed bytreating each row as an independent bionomial variable. In suchinstances, the true difference in proportions, termed π₁-π₂, comparesthe probabilities in the two rows. This difference falls between −1 and+1. It equals zero when π₁=π₂; that is, when the selection of kinases inthe sample from the population is independent of the kinase annotation.Of the N₁=25,000 biomarkers in the population, 800 are kinases, aproportion of p₁=800/25,000=0.032. Of the N₂=500 biomarkers in thesample identified using a data analysis algorithm, 50 are kinases, aproportion p₂ of 50/500=0.10. The sample difference of proportions is0.032−0.10=−0.068. In accordance with Agresti, when the counts in thetwo rows are independent binomial samples, the estimated standard errorof p₁-p₂ is:

${\hat{\sigma}\; \left( {p_{1} - p_{2}} \right)} = \sqrt{\frac{p_{1}\left( {1 - p_{1}} \right)}{N_{1}} + \frac{p_{2}\left( {1 - p_{2}} \right)}{N_{2}}}$

where N₁ and N₂ are the samples sizes for the population and the sampleselected by data analysis algorithm, respectively. The standard errordecreases, and hence the estimate of π₁-π₂ improves, as the sample sizesincrease. A large-sample (100(1−α))% confidence interval for π₁-π₂ is

(p₁ + p₂) ± z_(a/2) = z_(0.025) = 1.96

Thus, for this example, the estimated error is

$\sqrt{{\frac{0.032\left( {1 - 0.032} \right)}{25,000} + \frac{0.10\left( {1 - 0.10} \right)}{500}} =}0.013$

and a 95% confidence interval for the true difference π₁-π₂ is−0.068±1.96(0.013), or −0.068±0.025. Since the 95% confidence intervalcontains only negative values, the conclusion can be reached thatkinases are enriched in the sample (the biomarker set produced by thedata analysis algorithm) relative to the population of 25,000biomarkers.

The two-way contingency table in the example above can be analysed usingmethods known in the art other than the one disclosed above. Forexample, the chi-square test for independence and/or Fisher's exact testcan be used to test the null hypothesis that the row and columnclassification variables of the two-way contingency table areindependent.

The term “X” in annotation rule 2 can be any form of annotation data. Inone embodiment, “X” is any biological pathway. As such the annotationdata based filtering rule has the following form.

Annotation Rule 3:

Select all biomarkers that are in any biological pathway that isenriched in the biomarker list.To determine whether a particular biological pathway is enriched, thenumber of biomarkers in a particular biological pathway in the sample iscompared with the number of biomarkers that are in the particularbiological pathway in the population using, for example, the two-waycontingency table analysis described above, or other techniques known inthe art. If the biological pathway is enriched in the sample, then allbiomarkers in the sample that are also in the biological pathway areretained for further analysis, in accordance with the annotation databased filtering rule.

An example of enrichment, in which it was shown that the proportion ofkinases in the sample was greater than the proportion of kinases in thepopulation across its entire 95% confidence interval has been given. Inone embodiment, biomarkers having a given annotation are consideredenriched in the sample relative to the population when the proportion ofbiomarkers having the annotation in the sample is greater than theproportion of biomarkers having the annotation in the population acrossits entire 95% degree confidence interval as determined by two-waycontingency table analysis. In another embodiment, biomarkers having agiven annotation are considered enriched in the sample relative to thepopulation if a p value as determined by the Fisher exact test,Chi-square test, or relative algorithms is 0.05 or less, 0.005 or lessor 0.0005 or less.

Another form of annotation data based filtering rule has the followingform:

Annotation Rule 4:

Select all biomarkers that are in biological pathway X.

In an embodiment, a set of biomarkers is determined using a dataanalysis algorithm. Exemplary data analysis algorithms are disclosed inSection 5.5. In addition, Section 6 describes certain tests that canalso serve as data analysis algorithms. These tests include, but are notlimited to a Wilcoxon test and the like with a statistically significantp value (e.g., 0.05 or less, 0.04, or less, etc.), and/or a requirementthat a biomarker exhibit a mean differential abundance betweenbiological samples obtained from converters and biological samplesobtained from nonconverters in a training population. Upon applicationof the data analysis algorithm, a set of biomarkers that discriminatesbetween converters and nonconverters is determined. Next, an annotationrule, for example annotation rule 4, is applied to the set ofdiscriminating biomarkers in order to further reduce the set ofbiomarkers. Those of skill in the art will appreciate that the order inwhich these rules are applied is generally not important. For example,annotation rule 4 can be applied first and then certain data analysisalgorithms can be applied, or vice versa. In some embodiments,biomarkers ultimately deemed as discriminating between converters andnonconverters satisfy each of the following criteria: (i) a p value of0.05 or less (p<0.05) as determined from a Wilcoxon adjusted test usingstatic (single time point) data; (ii) a mean-fold change of 1.2 orgreater between converters and nonconverters across the training setusing static (single time point data), and (iii) present in a specificbiological pathway. See also, Section 6.7, infra, for a detailedexample. In this example, there is no requirement that members of thepathway are enriched in the set of biomarkers identified by the dataanalysis algorithms. Furthermore, it is noted that criteria (i) and (ii)are forms of data analysis algorithms and criterion (iii) is aannotation data based filtering rule.

In another embodiment, once a list of discriminating biomarkers isidentified, the biomarkers can then be used to determine the identity ofthe particular biological pathways from which the discriminatingbiomarkers are implicated. In certain embodiments, annotation data-basedfiltering rules are applied to the list of discriminating biomarkersidentified by the methods of the present invention (e.g., the methodsdescribed in Sections 5.4, 5.5 and 6). Such annotation data-basedfiltering rules identify the particular biological pathway or pathwaysthat are enriched in the discriminating list of biomarkers identified bythe data analysis algorithms. In an exemplary embodiment of theinvention, DAVID 2.0 software, available at apps1.niaid.nih.gov/david/,is used to identify and apply such annotation data-based filtering rulesto the set of biomarkers identified by the data analysis algorithms inorder to identify pathways that are enriched in the set. In someembodiments, those biomarkers that are in an enriched biological pathwayare selected for use as discriminating biomarkers in the kits of thepresent invention.

In some embodiments of the present invention, biomarkers that are inbiological pathways that are enriched in the biomarker set determined byapplication of a data analysis algorithm to a training population thatincludes converters and nonconverters can be used as filtering step toreduce the number of biomarkers in the set. In one such approach,biological samples from subjects in a training population are obtainedusing, e.g., any of one or more of the methods described in Section 5.4,supra, and in Section 6, infra. In accordance with this embodiment, anucleic acid array, such as a cDNA microarray, may be employed togenerate features of biomarkers in a biomarker profile by detecting theexpression of any one or more of the genes known to be or suspected tobe involved in the selected biological pathways. Data derived from thecDNA microarray analysis may then be analyzed using any one or more ofthe analysis algorithms described in Section 5.5, supra, to identifybiomarkers whose features discriminate between converters andnonconverters. Biomarkers whose corresponding feature values are capableof discriminating, for example, between converters (i.e., SIRS patientswho subsequently develop sepsis) and non-converters (i.e., SIRS patientswho do not subsequently develop sepsis) can thus be identified andclassified as discriminating biomarkers. Biomarkers that are in enrichedbiological pathways can be selected from this set by applying Annotationrule 3, from above. Representative biological pathways that could befound include, for example, genes involved in the Th1/Th2 celldifferentiation pathway). In one embodiments, biomarkers ultimatelydeemed as discriminating between converters and nonconverters satisfyeach of the following criteria: (i) a p value of 0.05 or less (p<0.05)as determined from a Wilcoxon adjusted test; (ii) a mean-fold change of1.2 or greater between converters and nonconverters across the trainingset, and (iii) present in a biological pathway that is enriched in theset of biomarkers derived by application of criteria (i) and (ii).

In some embodiments of the present invention, annotation data basedfiltering rules are used to identify biological pathways that areenriched in a given biomarker set. This biomarker set can be, forexample, a set of biomarkers that is identified by application of a dataanalysis algorithm to training data comprising converters andnonconverters. Then, biomarkers in these enriched biological pathwaysare analyzed using any of the data analysis algorithms disclosed hereinin order to identify biomarkers that discriminate between converters andnonconverters. In some instances, some of the biomarkers analyzed in theenriched biological pathways were not among the biomarkers in theoriginal given biomarker set. In some instances, some of the biomarkersin the enriched biological pathways are among the biomarkers in theoriginal given biomarker set. In some embodiments, a secondary assay isused to collect feature data for biomarkers that are in enrichedpathways and it is this data that is used to determine whether thebiomarkers in the enriched biological pathways discriminate betweenconverters and nonconverters.

In some embodiments, biomarkers in biological pathways of interest areidentified. In one example, genes involved in the Th1/Th2 celldifferentiation pathway are identified. Then, these biomarkers areevaluated using the data analysis algorithms disclosed herein todetermine whether they discriminate between converters andnonconverters.

5.11 Representative Embodiment in Accordance with the Present Invention

Sections 6.11 through 6.13 identify a number of biomarkers that are ofinterest in one embodiment in accordance with the present invention.Specifically, one embodiment of the present invention comprises the 10biomarkers identified in Table 48 of Section 6.11.1, the 34 biomarkerslisted in Table 59 of Section 6.11.2, and the 10 biomarkers listed inTable 93 of Section 6.13.1, below. Table 48 and Table 93 each identifyMMP9 as a discriminating biomarker. Thus, the total number of biomarkersin Table I is one less than the sum of the biomarkers identified inTables 48, Table 59, and Table 93, (34+10+10−1) or 53. These biomarkersare reproduced in Table I, below. Section 5.11.1 provides details oneach of the individual biomarkers. Section 5.11.2, below, provides moredetails on select combinations of the biomarkers listed in Tables I, J,and K. Each of the biomarkers listed in Table I were selected based onthe experimental results summarized in Sections 6.11 through 6.13. Insome experiments, the identified biomarkers were proteins or fragmentsthereof. Such protein biomarkers, which discriminate between sepsis andSIRS, are listed in Table I with a “P” designation in column 5. In someexperiments, the identified biomarkers were nucleic acids or fragmentthereof. Such nucleic acid biomarkers, which discriminate between sepsisand SIRS, are listed in Table I with an “N” designation in column 5. Asindicated above, one biomarker MMP9, was identified both as a proteinand as a nucleic acid biomarker. Table J below lists the biomarkers inaccordance with one embodiment of the present invention in which thebiomarkers were discovered using nucleic acid based assays described inSection 6, such as RT-PCR. Table K below lists the biomarkers inaccordance with one embodiment of the present invention in which thebiomarkers were discovered using protein based assays, described inSection 6, such as bead assays. One embodiment of the inventioncomprises at least 3, 4, 5, 6, 7, 8, 9, or 10 biomarkers from any one ofTables 48, 59, or 93.

Unless indicated in specific embodiments below, the biomarkers of TablesI, J and K are not limited by their physical form in the experimentssummarized in Sections 6.11 through 6.13. For example, although thediscriminatory nature of a biomarker may have been discovered by theabundance of the biomarker, in nucleic acid form, in a nucleic acidassay such as RT-PCR and accordingly listed in Table I on this basiswith an “N” designation in column 5 of Table I, the physicalmanifestation of the biomarker in the methods, kits, and biomarkerprofiles of the present invention is not limited to nucleic acids.Rather, any physical manifestation of the biomarker as defined for theterm “biomarker” in Section 5.1 is encompassed in the present invention.Column 6 of Table I indicates, based on the data summarized in Section 6below, whether the biomarker is up-regulated or down-regulated in thesubjects that will convert to sepsis (the converters) relative to thesubjects that will not convert (the SIRS subjects). Thus, if a biomarkerhas the designation UP, in column 6, that means that the biomarker, inthe form indicated in column 5, was, on average, more abundant insubjects that will convert to sepsis (sepsis subjects) relative tosubjects that will not convert to sepsis (SIRS subjects). Furthermore,if a biomarker has the designation DOWN, in column 6, that means thatthe biomarker, in the form indicated in column 5, was, on average, lessabundant in subjects that will convert to sepsis (sepsis subjects),relative to subjects that will not convert to sepsis (SIRS subjects).

TABLE I Biomarkers in accordance with an embodiment of the presentinvention. Gene Protein Gene Accession Accession Regulation Symbol GeneName Number Number Source in SEPSIS 1 2 3 4 5 6 AFP ALPHA-FETOPROTEINNM_001134 CAA79592 P UP ANKRD22 ANKYRIN REPEAT NM_144590 NP_653191 N UPDOMAIN 22 ANXA3 ANNEXIN A3 NM_005139 NP_005130 N UP APOC3 APOLIPOPROTEINCIII NM_000040 CAA25648 P DOWN ARG2 ARGINASE TYPE II NM_001172 CAG38787N UP B2M BETA-2 NM_004048 AAA51811 P UP MICROGLOBULIN BCL2A1BCL2-RELATED NM_004049 NP_004040 N UP PROTEIN A1 CCL5 CHEMOKINE (C-CNM_002985 NP_002976 N DOWN MOTIF) LIGAND 5 CD86 CD86 ANTIGEN (CD28NM_006889 NP_008820 N DOWN ANTIGEN LIGAND 2, NM_175862 NP_787058 B7-2ANTIGEN) CEACAM1 CARCINOEMBRYONIC NM_001712 NP_001703 N UPANTIGEN-RELATED CELL ADHESION MOLECULE 1 CRP C REACTIVE PROTEINNM_000567 CAA39671 P UP CRTAP CARTILAGE- NM_006371 NP_006362 N DOWNASSOCIATED PROTEIN CSF1R COLONY NM_005211 NP_005202 N DOWN STIMULATINGFACTOR 1 RECEPTOR, FORMERLY MCDONOUGH FELINE SARCOMA VIRAL (V- FMS)ONCOGENE HOMOLOG FAD104 FIBRONECTIN TYPE NM_022763 NP_073600 N UP IIIDOMAIN CONTAINING 3B (FNDC3B) FCGR1A FC FRAGMENT OF NM_000566 NP_000557N UP IGG, HIGH AFFINITY IA GADD45A GROWTH ARREST NM_001924 NP_001915 NUP AND DNA-DAMAGE- INDUCIBLE, ALPHA GADD45B GROWTH ARREST- NM_015675NP_056490 N UP AND DNA DAMAGE- INDUCIBLE GENE GADD45 HLA-DRA MAJORNM_002123 NP_002114 N DOWN HISTOCOMPATIBILITY COMPLEX, CLASS II, DRALPHA IFNGR1 INTERFERON GAMMA NM_000416 NP_000407 N UP RECEPTOR 1 IL1RNINTERLEUKIN = 1 NM_000577, AAN87150 N UP RECEPTOR NM_173841, ANTAGONISTGENE NM_173842, NM_173843 IL-6 INTERLEUKIN 6 NM_000600 NP_000591 P UPIL-8 INTERLEUKIN 8 M28130 AAA59158 P UP IL-10 INTERLEUKIN 10 NM_000572CAH73907 P UP IL10RA INTERLEUKIN 10 NM_001558 NP_001549 N DOWN RECEPTOR,ALPHA IL18R1 INTERLEUKIN 18 NM_003855 NP_003846 N UP RECEPTOR 1 INSL3INSULIN-LIKE 3 NM_005543 NP_005534 N UP (LEYDIG CELL) IRAK2INTERLEUKIN-1 NM_001570 NP_001561 N UP RECEPTOR- ASSOCIATED KINASE 2IRAK4 INTERLEUKIN-1 NM_016123 NP_057207 N UP RECEPTOR- ASSOCIATED KINASE4 ITGAM INTEGRIN, ALPHA M NM_000632 NP_000623 N UP (COMPLEMENT COMPONENTRECEPTOR 3, ALPHA; ALSO KNOWN AS CD11B (P170), MACROPHAGE ANTIGEN ALPHAPOLYPEPTIDE) JAK2 JANUS KINASE 2 (A NM_004972 NP_004963 N UP PROTEINTYROSINE KINASE) LDLR LOW DENSITY NM_000527 NP_000518 N UP LIPOPROTEINRECEPTOR LY96 LYMPHOCYTE NM_015364 NP_056179 N UP ANTIGEN 96 MAP2K6MITOGEN- NM_002758 NP_002749 N UP ACTIVATED PROTEIN NM_031988 NP_114365KINASE KINASE 6 MAPK14 MAPK14 MITOGEN- NM_001315 NP_001306 N UPACTIVATED PROTEIN NM_139012 NP_620581 KINASE 14 NM_139013 NP_620582NM_139014 NP_620583 MCP1 MONOCYTE AF493698, AAQ75526 P UPCHEMOATTRACTANT AF493697 PROTEIN 1 MKNK1 MAP KINASE NM_003684 NP_003675N UP INTERACTING NM_198973 NP_945324 SERINE/THREONINE KINASE 1 MMP9MATRIX NM_004994 NP_004985 N/P UP (both METALLOPROTEINASE protein and 9(GELATINASE B, nucleic 92KDA GELATINASE, acid) 92KDA TYPE IVCOLLAGENASE) NCR1 NATURAL NM_004829 NP_004820 N UP CYTOTOXICITYTRIGGERING RECEPTOR 1 OSM ONCOSTATIN M NM_020530 NP_065391 N UP PFKFB36-PHOSPHOFRUCTO- NM_004566 NP_004557 N UP 2-KINASE/FRUCTOSE-2,6-BISPHOSPHATASE 3 PRV1 NEUTROPHIL- NM_020406 NP_065139 N UP SPECIFICANTIGEN 1 (POLYCYTHEMIA RUBRA VERA 1) PSTPIP2 PROLINE/SERINE/ NM_024430NP_077748 N UP THREONINE PHOSPHATASE- INTERACTING PROTEIN 1 (PROLINE-SERINE-THREONINE PHOSPHATASE INTERACTING PROTEIN 2) SOCS3 SUPPRESSOR OFNM_003955 NP_003946 N UP CYTOKINE SIGNALING 3 SOD2 SUPEROXIDE NM_000636NP_000627 N UP DISMUTASE 2, MITOCHONDRIAL TDRD9 TUDOR DOMAIN NM_153046NP_694591 N UP CONTAINING 9 TGFBI TRANSFORMING NM_000358 NP_000349 NDOWN GROWTH FACTOR, BETA-1 (TRANSFORMING GROWTH FACTOR, BETA-INDUCED,68KDA) TIFA TRAF-INTERACTING NM_052864 NP_443096 N UP PROTEIN WITH AFORKHEAD- ASSOCIATED DOMAIN TIMP1 TISSUE INHIBITOR OF NM_003254 AAA75558P UP METALLOPROTEINASE 1 TLR4 TOLL-LIKE AH009665 AAF05316 N UP RECEPTOR4 TNFRSF6 TUMOR NECROSIS NM_152877 NP_000034 N UP FACTOR RECEPTORSUPERFAMILY, MEMBER 6 TNFSF10 TUMOR NECROSIS NM_003810 NP_003801 N UPFACTOR (LIGAND) SUPERFAMILY, MEMBER 10 TNFSF13B TUMOR NECROSIS NM_006573NP_006564 N UP FACTOR (LIGAND) SUPERFAMILY, MEMBER 13B VNN1 VANIN 1NM_004666 NP_004657 N UP

Each of the sequences, genes, proteins, and probesets identified inTable I is hereby incorporated by reference.

TABLE J Biomarkers identified based on the ability of the nucleic acidform of the biomarker to discriminate between SIRS and sepsis GeneProtein Gene Accession Accession Symbol Gene Name Number Number 1 2 3 4FCGR1A FC FRAGMENT OF NM_000566 NP_000557 IGG, HIGH AFFINITY IA MMP9MATRIX NM_004994 NP_004985 METALLOPROTEINASE 9 IL18R1 INTERLEUKIN 18NM_003855 NP_003846 RECEPTOR 1 ARG2 ARGINASE TYPE II NM_001172 CAG38787IL1RN INTERLEUKIN-1 NM_000577, AAN87150 RECEPTOR NM_173841, ANTAGONISTGENE NM_173842, NM_173843 TNFSF13B TUMOR NECROSIS NM_006573 NP_006564FACTOR SUPERFAMILY, MEMBER 13B ITGAM INTEGRIN, ALPHA M NM_000632NP_000623 TGFB1 TRANSFORMING NM_000358 NP_000349 GROWTH FACTOR, BETA-1CD86 CD86 ANTIGEN NM_006889 NP_008820 NM_175682 NP_787058 TLR4 TOLL-LIKEAH009665 AAF05316 RECEPTOR 4 BCL2-RELATED NM_004049 NP_004040 PROTEIN A1CCL5 CHEMOKINE (C-C NM_002985 NP_002976 MOTIF) LIGAND 5 CSF1R COLONYNM_005211 NP_005202 STIMULATING FACTOR 1 RECEPTOR, FORMERLY MCDONOUGHFELINE SARCOMA VIRAL (V- FMS) ONCOGENE HOMOLOG GADD45A GROWTH ARRESTNM_001924 NP_001915 AND DNA-DAMAGE- INDUCIBLE, ALPHA GADD45B GROWTHARREST- NM_015675 NP_056490 AND DNA DAMAGE- INDUCIBLE GENE GADD45 IFNGR1INTERFERON NM_000416 NP_000407 GAMMA RECEPTOR 1 IL10RA INTERLEUKIN 10NM_001558 NP_001549 RECEPTOR, ALPHA IRAK2 INTERLEUKIN-1 NM_001570NP_001561 RECEPTOR- ASSOCIATED KINASE 2 IRAK4 INTERLEUKIN-1 NM_016123NP_057207 RECEPTOR- ASSOCIATED KINASE 4 JAK2 JANUS KINASE 2 (A NM_004972NP_004963 PROTEIN TYROSINE KINASE) LY96 LYMPHOCYTE NM_015364 NP_056179ANTIGEN 96 MAP2K6 MITOGEN- NM_002758 NP_002749 ACTIVATED PROTEINNM_031988 NP_114365 KINASE 6 MAPK14 MAPK14 MITOGEN- NM_001315 NP_001306ACTIVATED PROTEIN NM_139012 NP_620581 KINASE 14 NM_139013 NP_620582NM_139014 NP_620583 MKNK1 MAP KINASE NM_003684 NP_003675 INTERACTINGNM_198973 NP_945324 SERINE/THREONINE KINASE 1 OSM ONCOSTATIN M NM_020530NP_065391 SOCS3 SUPPRESSOR OF NM_003955 NP_003946 CYTOKINE SIGNALING 3TDRD9 TUDOR DOMAIN NM_153046 NP_694591 CONTAINING 9 TNFRSF6 TUMORNECROSIS NM_152877 NP_000034 FACTOR RECEPTOR SUPERFAMILY, MEMBER 6TNFSF10 TUMOR NECROSIS NM_003810 NP_003801 FACTOR (LIGAND) SUPERFAMILY,MEMBER 10 ANKRD22 ANKYRIN REPEAT NM_144590 NP_653191 DOMAIN 22 ANXA3ANNEXIN A3 NM_005139 NP_005130 CEACAM1 CARCINOEMBRYONIC NM_001712NP_001703 ANTIGEN-RELATED CELL ADHESION MOLECULE 1 LDLR LOW DENSITYNM_000527 NP_000518 LIPOPROTEIN RECEPTOR PFKFB3 6-PHOSPHOFRUCTO-NM_004566 NP_004557 2-KINASE/FRUCTOSE- 2,6-BISPHOSPHATASE 3 PRV1NEUTROPHIL- NM_020406 NP_065139 SPECIFIC ANTIGEN 1 (POLYCYTHEMIA RUBRAVERA 1) PSTPIP2 PROLINE/SERINE/ NM_024430 NP_077748 THREONINEPHOSPHATASE- INTERACTING PROTEIN 1 (PROLINE- SERINE-THREONINEPHOSPHATASE INTERACTING PROTEIN 2) TIFA TRAF-INTERACTING NM_052864NP_443096 PROTEIN WITH A FORKHEAD- ASSOCIATED DOMAIN VNN1 VANIN 1NM_004666 NP004657 NCR1 NATURAL NM_004829 NP_004820 CYTOTOXICITYTRIGGERING RECEPTOR 1 FAD104 FIBRONECTIN TYPE NM_022763 NP_073600 IIIDOMAIN CONTAINING 3B (FNDC3B) INSL3 INSULIN-LIKE 3 NM_005543 NP_005534(LEYDIG CELL) CRTAP CARTILAGE- NM_006371 NP_006362 ASSOCIATED PROTEINHLA-DRA MAJOR NM_002123 NP_002114 HISTOCOMPATIBILITY COMPLEX, CLASS II,DR ALPHA SOD2 SUPEROXIDE NM_000636 NP_000627 DISMUTASE 2, MITOCHONDRIAL

TABLE K Biomarkers identified based on the ability of the protein formof the biomarker to discriminate between SIRS and sepsis Gene ProteinGene Accession Accession Symbol Gene Name Number Number 1 2 3 4 IL-6INTERLEUKIN 6 NM_000600 NP_000591 IL-8 INTERLEUKIN 8 M28130 AAA59158 CRPC Reactive protein CAA39671 NM_000567 IL-10 INTERLEUKIN 10 NM_000572CAH73907 APOC3 APOLIPOPROTEIN CIII NM_000040 CAA25648 MMP9 MATRIXNM_004994 NP_004985 METALLOPROTEINASE 9 (GELATINASE B, 92KDA GELATINASE,92KDA TYPE IV COLLAGENASE) TIMP1 TISSUE INHIBITOR OF NM_003254 AAA75558METALLOPROTEINASE 1 MCP1 MONOCYTE AF493698, AAQ75526 CHEMOATTRACTANTAF493697 PROTEIN 1 AFP ALPHA-FETOPROTEIN NM_001134 CAA79592 B2M BETA-2MICROGLOBULIN NM_004048 AAA51811

5.11.1 Biomarker Descriptions

The references for the biomarkers in this section merely provideexemplary sequences for the biomarkers set forth in the presentapplication.

The nucleotide sequence of AFP (identified by accession no. NM_001134)is disclosed in, e.g., Beattie et al., 1982, “Structure and evolution ofhuman alpha-fetoprotein deduced from partial sequence of cloned cDNA”Gene 20 (3): 415-422, Harper, M. E. et al., 1983, “Linkage of theevolutionarily-related serum albumin and alpha-fetoprotein genes withinq11-22 of human chromosome 4,” Am. J. Hum. Genet. 35 (4):565-572,Morinaga, T. et al., 1983, “Primary structures of humanalpha-fetoprotein and its mRNA,” Proc. Natl. Acad. Sci. U.S.A. 80(15):4604-4608, and the amino acid sequence of AFP (identified byaccession no. CAA79592) is disclosed in, e.g., McVey, 1993, DirectSubmission, Clinical Research Centre, Haemostasis Research Group,Watford Road, Harrow, UK, HA1 3UJ, McVey et al., 1993, “A G-->Asubstitution in an HNF I binding site in the human alpha-fetoproteingene is associated with hereditary persistence of alpha-fetoprotein(HPAFP),” Hum. Mol. Genet. 2 (4): 379-384, each of which is incorporatedby reference herein in its entirety.

The nucleotide sequence of ANKRD22 (identified by accession no.NM_144590) is disclosed in, e.g., Strausberg, 2002, “Homo sapiensankyrin repeat domain 22, mRNA (cDNA clone MGC:22805 IMAGE:3682099),”unpublished, and the amino acid sequence of ANKRD22 (identified byaccession no. NP_653191) is disclosed in, e.g., Strausberg, 2002, “Homosapiens ankyrin repeat domain 22, mRNA (cDNA clone MGC:22805IMAGE:3682099),” unpublished, each of which is incorporated by referenceherein in its entirety.

The nucleotide sequence of ANXA3 (identified by accession no. NM_005139)is disclosed in, e.g., Pepinsky, R. B. et al., 1988,” Five distinctcalcium and phospholipid binding proteins share homology with lipocortinI,” J. Biol. Chem. 263 (22): 10799-10811, Tait, J. F. et al., 1988,“Placental anticoagulant proteins: isolation and comparativecharacterization four members of the lipocortin family,” Biochemistry 27(17):6268-6276, Ross, T. S. et al., 1990, “Identity of inositol1,2-cyclic phosphate 2-phosphohydrolase with lipocortin III,” Science248 (4955):605-607, and the amino acid sequence of ANXA3 (identified byaccession no. NP_005130) is disclosed in, e.g., Pepinsky, R. B et al.,1988,” Five distinct calcium and phospholipid binding proteins sharehomology with lipocortin I,” J. Biol. Chem. 263 (22): 10799-10811, Tait,J. F. et al., 1988, “Placental anticoagulant proteins: isolation andcomparative characterization four members of the lipocortin family,”Biochemistry 27 (17):6268-6276, Ross, T. S. et al., 1990, “Identity ofinositol 1,2-cyclic phosphate 2-phosphohydrolase with lipocortin III,”Science 248 (4955):605-607, each of which is incorporated by referenceherein in its entirety.

The nucleotide sequence of Apolipoprotein CIII (APOC3) (identified byaccession no. NM_000040) is disclosed in, e.g., Ruiz-Narvaez. et al.,2005 “APOC3/A5 haplotypes, lipid levels, and risk of myocardialinfarction in the Central Valley of Costa Rica,” J. Lipid Res. 46 (12),2605-2613; Garenc et al., 2005, “Effect of the APOC3 Sst I SNP onfasting triglyceride levels in men heterozygous for the LPL P207Ldeficiency,” Eur. J. Hum. Genet. 13, 1159-1165; Baum. et al., 2005,“Effect of hepatic lipase-514C->T polymorphism and its interactions withapolipoprotein C3-482C->T and apolipoprotein E exon 4 polymorphisms onthe risk of nephropathy in chinese type 2 diabetic patients,” DiabetesCare 28, 1704-1709, and the amino acid sequence of APOC3 (identified byaccession no. CAA25648) is disclosed in, e.g., Protter et al., 1984,“Isolation and sequence analysis of the human apolipoprotein CIII geneand the intergenic region between the apo AI and apo CIII,” DNA 3,449-456, each of which is incorporated by reference herein in itsentirety.

The nucleotide sequence of ARG2 (identified by accession no. NM_001172)is disclosed in, e.g., Gotoh et al., 1996 “Molecular cloning of cDNA fornonhepatic mitochondrial arginase (arginase II) and comparison of itsinduction with nitric oxide synthase in a murine macrophage-like cellline,” FEBS Lett. 395 (2-3):119-122, Vockley et al., 1996, “Cloning andcharacterization of the human type II arginase gene,” Genomics 38(2):118-123, Gotoh et al., 1997, “Chromosomal localization of the humanarginase II gene and tissue distribution of its mRNA,” Biochem. Biophys.Res. Commun. 233 (2):487-491, and the amino acid sequence of ARG2(identified by accession no. CAG38787) is disclosed in, e.g., Halleck etal., 2004, Direct Submission, RZPD Deutsches Ressourcenzentrum fuerGenomforschung GmbH, Im Neuenheimer Feld 580, D-69120 Heidelberg,Germany, Halleck et al., unpublished, “Cloning of human full openreading frames in Gateway™ system entry vector (pDONR201),” each ofwhich is incorporated by reference herein in its entirety.

The nucleotide sequence of B2M (identified by accession no. NM_004048)is disclosed in, e.g., Krangel, M. S. et al., 1979, “Assembly andmaturation of HLA-A and HLA-B antigens in vivo,” Cell 18 (4):979-991,Suggs, S. V. et al., 1981, “Use of synthetic oligonucleotides ashybridization probes: isolation of cloned cDNA sequences for human beta2-microglobulin,” Proc. Natl. Acad. Sci. U.S.A. 78 (11):6613-6617, Rosa,F. et al., 1983, “The beta2-microglobulin mRNA in human Daudi cells hasa mutated initiation codon but is still inducible by interferon,” EMBOJ. 2 (2):239-243, and the amino acid sequence of B2M (identified byaccession no. AAA51811) is disclosed in, e.g., Gussow, D. et al., 1987,“The human beta 2-microglobulin gene. Primary structure and definitionof the transcriptional unit,” J. Immunol. 139 (9): 3132-3138, each ofwhich is incorporated by reference herein in its entirety.

The nucleotide sequence of BCL2A1 (identified by accession no.NM_004049) is disclosed in, e.g., Lin, E. Y. et al., 1993,“Characterization of A1, a novel hemopoietic-specific early-responsegene with sequence similarity to bcl-2,” J. Immunol. 151 (4):1979-1988,Savitsky, K. et al., “The complete sequence of the coding region of theATM gene reveals similarity to cell cycle regulators in differentspecies,” Hum. Mol. Genet. 4 (11):2025-2032, Choi, S. S. et al., 1995,“A novel Bcl-2 related gene, Bfl-1, is overexpressed in stomach cancerand preferentially expressed in bone marrow,” Oncogene 11 (9):1693-1698,and the amino acid sequence of BCL2A1 (identified by accession no.NP_004040) is disclosed in, e.g., Lin, E. Y. et al., 1993,“Characterization of A1, a novel hemopoietic-specific early-responsegene with sequence similarity to bcl-2,” J. Immunol. 151 (4):1979-1988,Savitsky, K. et al., “The complete sequence of the coding region of theATM gene reveals similarity to cell cycle regulators in differentspecies,” Hum. Mol. Genet. 4 (11):2025-2032, Choi, S. S. et al., 1995,“A novel Bcl-2 related gene, Bfl-1, is overexpressed in stomach cancerand preferentially expressed in bone marrow,” Oncogene 11 (9):1693-1698,each of which is incorporated by reference herein in its entirety.

The nucleotide sequence of CCL5 (identified by accession no. NM_002985)is disclosed in, e.g., Schall, T. J. et al., 1988, “A human Tcell-specific molecule is a member of a new gene family,” J. Immunol.141 (3):1018-1025, Donlon, T. A. et al., 1990, “Localization of a humanT-cell-specific gene, RANTES (D17S136E), to chromosome 17q11.2-q12,”Genomics 6 (3):548-553, Kameyoshi, Y. et al., 1992, “Cytokine RANTESreleased by thrombin-stimulated platelets is a potent attractant forhuman eosinophils,” J. Exp. Med. 176 (2):587-592, and the amino acidsequence of CCL5 (identified by accession no. NP_002976) is disclosedin, e.g., Schall, T. J. et al., 1988, “A human T cell-specific moleculeis a member of a new gene family,” J. Immunol. 141 (3):1018-1025,Donlon, T. A. et al., 1990, “Localization of a human T-cell-specificgene, RANTES (D17S136E), to chromosome 17q11.2-q12,” Genomics 6(3):548-553, Kameyoshi, Y. et al., 1992, “Cytokine RANTES released bythrombin-stimulated platelets is a potent attractant for humaneosinophils,” J. Exp. Med. 176 (2):587-592, each of which isincorporated by reference herein in its entirety.

The nucleotide sequence of CD86 (identified by accession nos. NM_006889,NM 175862) is disclosed in, e.g., Azuma, M. et al., 1993, “B70 antigenis a second ligand for CTLA-4 and CD28,” Nature 366 (6450):76-79,Freeman, G. J. et al., 1993, “Cloning of B7-2: a CTLA-4 counter-receptorthat costimulates human T cell proliferation,” Science 262(5135):909-911, Chen, C. et al., 1994, “Molecular cloning and expressionof early T cell costimulatory molecule-1 and its characterization asB7-2 molecule,” J. Immunol. 152 (10):4929-4936, and the amino acidsequence of CD86 (identified by accession nos. NP_008820, NP_787058) isdisclosed in, e.g., Azuma, M. et al., 1993, “B70 antigen is a secondligand for CTLA-4 and CD28,” Nature 366 (6450):76-79, Azuma, M. et al.,1993, “B70 antigen is a second ligand for CTLA-4 and CD28,” Nature 366(6450):76-79, Freeman, G. J. et al., 1993, “Cloning of B7-2: a CTLA-4counter-receptor that costimulates human T cell proliferation,” Science262 (5135):909-911, Chen, C. et al., 1994, “Molecular cloning andexpression of early T cell costimulatory molecule-1 and itscharacterization as B7-2 molecule,” J. Immunol. 152 (10):4929-4936, eachof which is incorporated by reference herein in its entirety.

The nucleotide sequence of CEACAM1 (identified by accession no.NM_001712) is disclosed in, e.g., Svenberg, T. et al., 1979,“Immunofluorescence studies on the occurrence and localization of theCEA-related biliary glycoprotein I (BGP I) in normal humangastrointestinal tissues,” Clin. Exp. Immunol. 36 (3):436-441, Hinoda,Y. et al., 1988, “Molecular cloning of a cDNA coding biliaryglycoprotein I: primary structure of a glycoprotein immunologicallycrossreactive with carcinoembryonic antigen,” Proc. Natl. Acad. Sci.U.S.A. 85 (18):6959-6963, Barnett, T. R. et al., 1989, “Carcinoembryonicantigens: alternative splicing accounts for the multiple mRNAs that codefor novel members of the carcinoembryonic antigen family,” J. Cell Biol.108 (2):267-276, and the amino acid sequence of CEACAM1 (identified byaccession no. NP_001703) is disclosed in, e.g., Svenberg, T. et al.,1979, “Immunofluorescence studies on the occurrence and localization ofthe CEA-related biliary glycoprotein I (BGP I) in normal humangastrointestinal tissues,” Clin. Exp. Immunol. 36 (3):436-441, Hinoda,Y. et al., 1988, “Molecular cloning of a cDNA coding biliaryglycoprotein I: primary structure of a glycoprotein immunologicallycrossreactive with carcinoembryonic antigen,” Proc. Natl. Acad. Sci.U.S.A. 85 (18):6959-6963, Barnett, T. R. et al., 1989, “Carcinoembryonicantigens: alternative splicing accounts for the multiple mRNAs that codefor novel members of the carcinoembryonic antigen family,” J. Cell Biol.108 (2):267-276, each of which is incorporated by reference herein inits entirety.

The nucleotide sequence of C Reactive Protein (CRP) (identified byaccession no. NM_000567) is disclosed in, e.g., Song et al., 2006,“C-reactive protein contributes to the hypercoagulable state in coronaryartery disease,” J. Thromb. Haemost. 4 (1), 98-106; Wakugawa et al.,2006, “C-reactive protein and risk of first-ever ischemic andhemorrhagic stroke in a general Japanese population: the HisayamaStudy,” Stroke 37, 27-32; Tong et al., 2005, “Association oftestosterone, insulin-like growth factor-I, and C-reactive protein withmetabolic syndrome in Chinese middle-aged men with a family history oftype 2 diabetes,” J. Clin. Endocrinol. Metab. 90, 6418-6423, and theamino acid sequence of CRP (identified by accession no. CAA39671 isdescribed in a direct submissiong by Tenchini et al., 1990, Tenchini M.L., Dipartimento di Biologia e Genetica per le Scienze mediche, viaViotti 3, 20133 Milano, Italy, each of which is incorporated byreference herein in its entirety.

The nucleotide sequence of CRTAP (identified by accession no. NM_006371)is disclosed in, e.g., Castagnola, P. et al., 1997, “Cartilageassociated protein (CASP) is a novel developmentally regulated chickembryo protein,” J. Cell. Sci. 110 (PT 12):1351-1359; Tonachini, L. etal., 1999, “cDNA cloning, characterization and chromosome mapping of thegene encoding human cartilage associated protein (CRTAP),” Cytogenet.Cell Genet. 87:(3-4); Morello, R. et al., 1999, “cDNA cloning,characterization and chromosome mapping of Crtap encoding the mousecartilage associated protein,” Matrix Biol. 18 (3):319-324, and theamino acid sequence of CRTAP (identified by accession no. NP_006362) isdisclosed in, e.g., Castagnola, P. et al., 1997, “Cartilage associatedprotein (CASP) is a novel developmentally regulated chick embryoprotein,” J. Cell. Sci. 110 (PT 12):1351-1359, Tonachini, L. et al.,1999, “cDNA cloning, characterization and chromosome mapping of the geneencoding human cartilage associated protein (CRTAP),” Cytogenet. CellGenet. 87:(3-4), Morello, R. et al., 1999, “cDNA cloning,characterization and chromosome mapping of Crtap encoding the mousecartilage associated protein,” Matrix Biol. 18 (3):319-324, each ofwhich is incorporated by reference herein in its entirety.

The nucleotide sequence of CSF1R (identified by accession no. NM_005211)is disclosed in, e.g., Verbeek, J. S. et al., 1985, “Human c-fmsproto-oncogene: comparative analysis with an abnormal allele,” Mol.Cell. Biol. 5 (2):422-426; Xu, D. Q. et al., 1985, “Restriction fragmentlength polymorphism of the human c-fms gene,” Proc. Natl. Acad. Sci.U.S.A. 82 (9):2862-2865; Sherr, C. J. et al., 1985, “The c-fmsproto-oncogene product is related to the receptor for the mononuclearphagocyte growth factor, CSF-1,” Cell 41 (3):665-676, and the amino acidsequence of CSF1R (identified by accession no. NP_005202) is disclosedin, e.g., Verbeek, J. S. et al., 1985, “Human c-fms proto-oncogene:comparative analysis with an abnormal allele,” Mol. Cell. Biol. 5(2):422-426, Xu, D. Q. et al., 1985, “Restriction fragment lengthpolymorphism of the human c-fms gene,” Proc. Natl. Acad. Sci. U.S.A. 82(9):2862-2865, Sherr, C. J. et al., 1985, “The c-fms proto-oncogeneproduct is related to the receptor for the mononuclear phagocyte growthfactor, CSF-1,” Cell 41 (3):665-676, each of which is incorporated byreference herein in its entirety.

The nucleotide sequence of FAD104 (identified by accession no.NM_022763) is disclosed in, e.g., Clark, H. F. et al., 2003, “Thesecreted protein discovery initiative (SPDI), a large-scale effort toidentify novel human secreted and transmembrane proteins: abioinformatics assessment,” Genome Res. 13 (10):2265-2270, Tominaga, K.et al., 2004, “The novel gene fad104, containing a fibronectin type IIIdomain, has a significant role in adipogenesis,” FEBS Lett. 577(1-2):49-54, and the amino acid sequence of FAD104 (identified byaccession no. NP_073600) is disclosed in, e.g., Clark, H. F. et al.,2003, “The secreted protein discovery initiative (SPDI), a large-scaleeffort to identify novel human secreted and transmembrane proteins: abioinformatics assessment,” Genome Res. 13 (10):2265-2270, Tominaga, K.et al., 2004, “The novel gene fad104, containing a fibronectin type IIIdomain, has a significant role in adipogenesis,” FEBS Lett. 577(1-2):49-54, each of which is incorporated by reference herein in itsentirety.

The nucleotide sequence of FCGR1A (identified by accession no.NM_000566) is disclosed in, e.g., Eizuru, Y. et al., 1988, “Induction ofFc (IgG) receptor(s) by simian cytomegaloviruses in human embryonic lungfibroblasts,” Intervirology 29 (6):339-345, Allen, J. M. et al., 1988,“Nucleotide sequence of three cDNAs for the human high affinity Fcreceptor (FcRI),” Nucleic Acids Res. 16 (24):11824, van de Winkel, J. G.et al., 1991, “Gene organization of the human high affinity receptor forIgG, Fc gamma RI (CD64). Characterization and evidence for a secondgene,” J. Biol. Chem. 266 (20):13449-1345, and the amino acid sequenceof FCGR1A (identified by accession no. NP_000557) is disclosed in, e.g.,Eizuru, Y. et al., 1988, “Induction of Fc (IgG) receptor(s) by simiancytomegaloviruses in human embryonic lung fibroblasts,” Intervirology 29(6):339-345, Allen, J. M. et al., 1988, “Nucleotide sequence of threecDNAs for the human high affinity Fc receptor (FcRI),” Nucleic AcidsRes. 16 (24):11824, van de Winkel, J. G. et al., 1991, “Geneorganization of the human high affinity receptor for IgG, Fc gamma RI(CD64). Characterization and evidence for a second gene,” J. Biol. Chem.266 (20):13449-1345, each of which is incorporated by reference hereinin its entirety.

The nucleotide sequence of GADD45A (identified by accession no.NM_001924) is disclosed in, e.g., Papathanasiou, M. A. et al., 1991,“Induction by ionizing radiation of the gadd45 gene in cultured humancells: lack of mediation by protein kinase C,” Mol. Cell. Biol. 11(2):1009-1016, Hollander, M. C. et al., 1993, “Analysis of the mammaliangadd45 gene and its response to DNA damage,” J. Biol. Chem. 268(32):24385-24393, Smith, M. L. et al., 1994, “Interaction of thep53-regulated protein Gadd45 with proliferating cell nuclear antigen,”Science 266 (5189):1376-1380, and the amino acid sequence of GADD45A(identified by accession no. NP_001915) is disclosed in, e.g.,Papathanasiou, M. A. et al., 1991, “Induction by ionizing radiation ofthe gadd45 gene in cultured human cells: lack of mediation by proteinkinase C,” Mol. Cell. Biol. 11 (2):1009-1016, Hollander, M. C. et al.,1993, “Analysis of the mammalian gadd45 gene and its response to DNAdamage,” J. Biol. Chem. 268 (32):24385-24393, Smith, M. L. et al., 1994,“Interaction of the p53-regulated protein Gadd45 with proliferating cellnuclear antigen,” Science 266 (5189):1376-1380, each of which isincorporated by reference herein in its entirety.

The nucleotide sequence of GADD45B (identified by accession no.NM_015675) is disclosed in, e.g., Abdollahi, A. et al., 1991, “Sequenceand expression of a cDNA encoding MyD118: a novel myeloiddifferentiation primary response gene induced by multiple cytokines,”Oncogene 6 (1): 165-167, Vairapandi, M. et al., 1996, “Thedifferentiation primary response gene MyD118, related to GADD45, encodesfor a nuclear protein which interacts with PCNA and p21WAF1/CIP1,”Oncogene 12 (12):2579-2594, Koonin, E. V., 1997, “Cell cycle andapoptosis: possible roles of Gadd45 and MyD118 proteins inferred fromtheir homology to ribosomal proteins,” J. Mol. Med. 75 (4):236-238, andthe amino acid sequence of GADD45B (identified by accession no.NP_056490) is disclosed in, e.g., Abdollahi, A. et al., 1991, “Sequenceand expression of a cDNA encoding MyD118: a novel myeloiddifferentiation primary response gene induced by multiple cytokines,”Oncogene 6 (1):165-167, Vairapandi, M. et al., 1996, “Thedifferentiation primary response gene MyD118, related to GADD45, encodesfor a nuclear protein which interacts with PCNA and p21WAF1/CIP1,”Oncogene 12 (12):2579-2594, Koonin, E. V., 1997, “Cell cycle andapoptosis: possible roles of Gadd45 and MyD118 proteins inferred fromtheir homology to ribosomal proteins,” J. Mol. Med. 75 (4):236-238, eachof which is incorporated by reference herein in its entirety.

The nucleotide sequence of HLA-DRA (identified by accession no.NM_002123) is disclosed in, e.g., Larhammar, D. et al., 1981,Evolutionary relationship between HLA-DR antigen beta-chains, HLA-A, B,C antigen subunits and immunoglobulin chains,” Scand. J. Immunol. 14(6):617-622, Wiman, K. et al., 1982, “Isolation and identification of acDNA clone corresponding to an HLA-DR antigen beta chain,” Proc. Natl.Acad. Sci. U.S.A. 79 (6):1703-1707, Larhammar, D. et al., 1982,“Complete amino acid sequence of an HLA-DR antigen-like beta chain aspredicted from the nucleotide sequence: similarities withimmunoglobulins and HLA-A, -B, and -C antigens,” Proc. Natl. Acad. Sci.U.S.A. 79 (12):3687-3691, and the amino acid sequence of HLA-DRA(identified by accession no. NP_002114) is disclosed in, e.g.,Larhammar, D. et al., 1981, Evolutionary relationship between HLA-DRantigen beta-chains, HLA-A, B, C antigen subunits and immunoglobulinchains,” Scand. J. Immunol. 14 (6):617-622, Wiman, K. et al., 1982,“Isolation and identification of a cDNA clone corresponding to an HLA-DRantigen beta chain,” Proc. Natl. Acad. Sci. U.S.A. 79 (6):1703-1707,Larhammar, D. et al., 1982, “Complete amino acid sequence of an HLA-DRantigen-like beta chain as predicted from the nucleotide sequence:similarities with immunoglobulins and HLA-A, -B, and -C antigens,” Proc.Natl. Acad. Sci. U.S.A. 79 (12):3687-3691, each of which is incorporatedby reference herein in its entirety.

The nucleotide sequence of IFNGR1 (identified by accession no.NM_000416) is disclosed in, e.g., Novick, D. et al., 1987, “The humaninterferon-gamma receptor. Purification, characterization, andpreparation of antibodies, each of which is incorporated by referenceherein in its entirety,” J. Biol. Chem. 262 (18): 8483-8487, Aguet, M.et al., 1988, “Molecular cloning and expression of the humaninterferon-gamma receptor,” Cell 55 (2): 273-280, Le Coniat, M. et al.,1989, “Human interferon gamma receptor 1 (IFNGR1) gene maps tochromosome region 6q23-6q24,” Hum. Genet. 84 (1):92-94, and the aminoacid sequence of IFNGR1 (identified by accession no. NP_000407) isdisclosed in, e.g., Novick, D. et al., 1987, “The human interferon-gammareceptor. Purification, characterization, and preparation ofantibodies,” J. Biol. Chem. 262 (18):8483-8487, Aguet, M. et al., 1988,“Molecular cloning and expression of the human interferon-gammareceptor,” Cell 55 (2): 273-280, Le Coniat, M. et al., 1989, “Humaninterferon gamma receptor 1 (IFNGR1) gene maps to chromosome region6q23-6q24,” Hum. Genet. 84 (1):92-94, each of which is incorporated byreference herein in its entirety.

The nucleotide sequence of IL1RN (identified by accession nos.NM_000577, NM_173841, NM_173842, NM_173843) is disclosed in, e.g.,Eisenberg, S. P. et al., 1990, “Primary structure and functionalexpression from complementary DNA of a human interleukin-1 receptorantagonist,” Nature 343 (6256):341-346, Carter, D. B. et al., 1990,“Purification, cloning, expression and biological characterization of aninterleukin-1 receptor antagonist protein,” Nature 344 (6267):633-638,Seckinger, P. et al., 1990, “Natural and recombinant human IL-1 receptorantagonists block the effects of IL-1 on bone resorption andprostaglandin production,” J. Immunol. 145 (12):4181-4184, and the aminoacid sequence of IL1RN (identified by accession no. AAN87150) isdisclosed in, e.g., Rieder, M. J. et al., 2002, Direct Submission,Genome Sciences, University of Washington, 1705 NE Pacific, Seattle,Wash. 98195, USA, each of which is incorporated by reference herein inits entirety.

The nucleotide sequence of IL-6 (identified by accession no. NM_000600)is disclosed in, e.g., Haegeman, G. et al., 1986, “Structural analysisof the sequence coding for an inducible 26-kDa protein in humanfibroblasts,” Eur. J. Biochem. 159 (3):625-632, Zilberstein, A. et al.,1986, “Structure and expression of cDNA and genes for humaninterferon-beta-2, a distinct species inducible by growth-stimulatorycytokines,” EMBO J. 5 (10):2529-2537, Hirano, T. et al., 1986,“Complementary DNA for a novel human interleukin (BSF-2) that induces Blymphocytes to produce immunoglobulin,” Nature 324 (6092):73-76, and theamino acid sequence of IL-6 (identified by accession no. NP_000591) isdisclosed in, e.g., Haegeman, G. et al., 1986, “Structural analysis ofthe sequence coding for an inducible 26-kDa protein in humanfibroblasts,” Eur. J. Biochem. 159 (3):625-632, Zilberstein, A. et al.,1986, “Structure and expression of cDNA and genes for humaninterferon-beta-2, a distinct species inducible by growth-stimulatorycytokines,” EMBO J. 5 (10):2529-2537, Hirano, T. et al., 1986,“Complementary DNA for a novel human interleukin (BSF-2) that induces Blymphocytes to produce immunoglobulin,” Nature 324 (6092):73-76, each ofwhich is incorporated by reference herein in its entirety.

The nucleotide sequence of IL-8 (identified by accession no. M28130) andthe amino acid sequence of IL-8 (identified by accession no. AAA59158)are each disclosed in, e.g., Mukaida et al., 1989, “Genomic structure ofthe human monocyte-derived neutrophil chemotactic factor IL-8,” J.Immunol. 143, 1366-1371 which is incorporated by reference herein in itsentirety.

The nucleotide sequence of IL-10 (identified by accession no. NM_000572)is disclosed in, e.g., Ghosh, S. et al., 1975, “Anaerobic acidogenesisof wastewater sludge,” Breast Cancer Res. Treat. 47 (1):30-45, Hsu, D.H. et al., 1990, “Expression of interleukin-10 activity by Epstein-Barrvirus protein BCRF1,” Science 250 (4982):830-832, Vieira, P. et al.,1991, “Isolation and expression of human cytokine synthesis inhibitoryfactor cDNA clones: homology to Epstein-Barr virus open reading frameBCRFI,” Proc. Natl. Acad. Sci. U.S.A. 88 (4):1172-1176, and the aminoacid sequence of IL-10 (identified by accession no. CAH73907) isdisclosed in, e.g., Tracey, A., 2005, Direct Submission, Wellcome TrustSanger Institute, Hinxton, Cambridgeshire, CB10 1SA, each of which isincorporated by reference herein in its entirety.

The nucleotide sequence of IL10RA (identified by accession no.NM_001558) is disclosed in, e.g., Tan, J. C. et al., 1993,“Characterization of interleukin-10 receptors on human and mouse cells,”J. Biol. Chem. 268 (28):21053-21059, Ho, A. S. et al., 1993, “A receptorfor interleukin 10 is related to interferon receptors,” Proc. Natl.Acad. Sci. U.S.A. 90 (23):11267-11271, Liu, Y. et al., 1994, “Expressioncloning and characterization of a human IL-10 receptor,” J. Immunol. 152(4):1821-1829, and the amino acid sequence of IL10RA (identified byaccession no. NP_001549) is disclosed in, e.g., Tan, J. C. et al., 1993,“Characterization of interleukin-10 receptors on human and mouse cells,”J. Biol. Chem. 268 (28):21053-21059, Ho, A. S. et al., 1993, “A receptorfor interleukin 10 is related to interferon receptors,” Proc. Natl.Acad. Sci. U.S.A. 90 (23):11267-11271, Liu, Y. et al., 1994, “Expressioncloning and characterization of a human IL-10 receptor,” J. Immunol. 152(4):1821-1829, each of which is incorporated by reference herein in itsentirety.

The nucleotide sequence of IL18R1 (identified by accession no.NM_003855) is disclosed in, e.g., Parnet, P. et al., 1996, “IL-1Rrp is anovel receptor-like molecule similar to the type I interleukin-1receptor and its homologues T1/ST2 and IL-1R AcP,” J. Biol. Chem. 271(8):3967-3970, Lovenberg, T. W. et al., 1996, “Cloning of a cDNAencoding a novel interleukin-1 receptor related protein (IL 1R-rp2),” J.Neuroimmunol. 70 (2):113-122, Torigoe, K. et al., 1997, “Purificationand characterization of the human interleukin-18 receptor,” J. Biol.Chem. 272 (41):25737-25742, and the amino acid sequence of IL18R1(identified by accession no. NP_003846) is disclosed in, e.g., Parnet,P. et al., 1996, “IL-1Rrp is a novel receptor-like molecule similar tothe type I interleukin-1 receptor and its homologues T1/ST2 and IL-1RAcP,” J. Biol. Chem. 271 (8):3967-3970, Lovenberg, T. W. et al., 1996,“Cloning of a cDNA encoding a novel interleukin-1 receptor relatedprotein (IL 1R-rp2),” J. Neuroimmunol. 70 (2):113-122, Torigoe, K. etal., 1997, “Purification and characterization of the humaninterleukin-18 receptor,” J. Biol. Chem. 272 (41):25737-25742, each ofwhich is incorporated by reference herein in its entirety.

The nucleotide sequence of INSL3 (identified by accession no. NM_005543)is disclosed in, e.g., Adham, I. M. et al., 1993, “Cloning of a cDNA fora novel insulin-like peptide of the testicular Leydig cells,” J. Biol.Chem. 268 (35):26668-26672, Burkhardt, E. et al., 1994, “Structuralorganization of the porcine and human genes coding for a Leydigcell-specific insulin-like peptide (LEY I-L) and chromosomallocalization of the human gene (INSL3),” Genomics 20 (1):13-19,Burkhardt, E. et al., 1994, “A human cDNA coding for the Leydiginsulin-like peptide (Ley I-L),” Hum. Genet. 94 (1):91-94, and the aminoacid sequence of INSL3 (identified by accession no. NP_005534) isdisclosed in, e.g., Adham, I. M. et al., 1993, “Cloning of a cDNA for anovel insulin-like peptide of the testicular Leydig cells,” J. Biol.Chem. 268 (35):26668-26672, Burkhardt, E. et al., 1994, “Structuralorganization of the porcine and human genes coding for a Leydigcell-specific insulin-like peptide (LEY I-L) and chromosomallocalization of the human gene (INSL3),” Genomics 20 (1):13-19,Burkhardt, E. et al., 1994, “A human cDNA coding for the Leydiginsulin-like peptide (Ley I-L),” Hum. Genet. 94 (1):91-94, each of whichis incorporated by reference herein in its entirety.

The nucleotide sequence of IRAK2 (identified by accession no. NM_001570)is disclosed in, e.g., Muzio, M. et al., 1997, “IRAK (Pelle) familymember IRAK-2 and MyD88 as proximal mediators of IL-1 signaling,”Science 278 (5343):1612-1615, Auron, P. E., 1998, “The interleukin 1receptor: ligand interactions and signal transduction,” Cytokine GrowthFactor Rev. 9 (3-4):221-237, Maschera, B. et al., 1999, “Overexpressionof an enzymically inactive interleukin-1-receptor-associated kinaseactivates nuclear factor-kappaB,” Biochem. J. 339 (PT 2):227-231, andthe amino acid sequence of IRAK2 (identified by accession no. NP_001561)is disclosed in, e.g., Muzio, M. et al., 1997, “IRAK (Pelle) familymember IRAK-2 and MyD88 as proximal mediators of IL-1 signaling,”Science 278 (5343):1612-1615, Auron, P. E., 1998, “The interleukin 1receptor: ligand interactions and signal transduction,” Cytokine GrowthFactor Rev. 9 (3-4):221-237, Maschera, B. et al., 1999, “Overexpressionof an enzymically inactive interleukin-1-receptor-associated kinaseactivates nuclear factor-kappaB,” Biochem. J. 339 (PT 2):227-231, eachof which is incorporated by reference herein in its entirety.

The nucleotide sequence of IRAK4 (identified by accession no. NM_016123)is disclosed in, e.g., Siu, G. et al., 1986, “Analysis of a human V betagene subfamily,” J. Exp. Med. 164 (5):1600-1614, Scanlan, M. J. et al.,1999, “Antigens recognized by autologous antibody in patients withrenal-cell carcinoma,” Int. J. Cancer 83 (4):456-464, Li, S. et al.,2002, “IRAK-4: a novel member of the IRAK family with the properties ofan IRAK-kinase,” Proc. Natl. Acad. Sci. U.S.A. 99 (8):5567-5572, and theamino acid sequence of IRAK4 (identified by accession no. NP_057207) isdisclosed in, e.g., Siu, G. et al., 1986, “Analysis of a human V betagene subfamily,” J. Exp. Med. 164 (5):1600-1614, Scanlan, M. J. et al.,1999, “Antigens recognized by autologous antibody in patients withrenal-cell carcinoma,” Int. J. Cancer 83 (4):456-464, Li, S. et al.,2002, “IRAK-4: a novel member of the IRAK family with the properties ofan IRAK-kinase,” Proc. Natl. Acad. Sci. U.S.A. 99 (8):5567-5572, each ofwhich is incorporated by reference herein in its entirety.

The nucleotide sequence of ITGAM (identified by accession no. NM_000632)is disclosed in, e.g., Micklem, K. J. et al., 1985, “Isolation ofcomplement-fragment-iC3 b-binding proteins by affinity chromatography.The identification of p150,95 as an iC3b-binding protein,” Biochem. J.231 (1):233-236, Pierce, M. W. et al., 1986, “N-terminal sequence ofhuman leukocyte glycoprotein Mo1: conservation across species andhomology to platelet IIb/IIIa,” Biochim. Biophys. Acta 874 (3):368-371,Arnaout, M. A. et al., 1988, “Molecular cloning of the alpha subunit ofhuman and guinea pig leukocyte adhesion glycoprotein Mol: chromosomallocalization and homology to the alpha subunits of integrins,” Proc.Natl. Acad. Sci. U.S.A. 85 (8):2776-2780, and the amino acid sequence ofITGAM (identified by accession no. NP_000623) is disclosed in, e.g.,Micklem, K. J. et al., 1985, “Isolation ofcomplement-fragment-iC3b-binding proteins by affinity chromatography.The identification of p150,95 as an iC3b-binding protein,” Biochem. J.231 (1):233-236, Pierce, M. W. et al., 1986, “N-terminal sequence ofhuman leukocyte glycoprotein Mol: conservation across species andhomology to platelet IIb/IIIa,” Biochim. Biophys. Acta 874 (3):368-371,Arnaout, M. A. et al., 1988, “Molecular cloning of the alpha subunit ofhuman and guinea pig leukocyte adhesion glycoprotein Mol: chromosomallocalization and homology to the alpha subunits of integrins,” Proc.Natl. Acad. Sci. U.S.A. 85 (8):2776-2780, each of which is incorporatedby reference herein in its entirety.

The nucleotide sequence of JAK2 (identified by accession no. NM_004972)is disclosed in, e.g., Wilks, A. F. et al., 1991, “Two novelprotein-tyrosine kinases, each with a second phosphotransferase-relatedcatalytic domain, define a new class of protein kinase,” Mol. Cell.Biol. 11 (4):2057-2065, Pritchard, M. A. et al., 1992, “Two members ofthe JAK family of protein tyrosine kinases map to chromosomes 1p31.3 and9p24,” Mamm. Genome 3 (1):36-38, Witthuhn, B. A. et al., 1993, “JAK2associates with the erythropoietin receptor and is tyrosinephosphorylated and activated following stimulation with erythropoietin,”Cell 74 (2):227-236, and the amino acid sequence of JAK2 (identified byaccession no. NP_004963) is disclosed in, e.g., Wilks, A. F. et al.,1991, “Two novel protein-tyrosine kinases, each with a secondphosphotransferase-related catalytic domain, define a new class ofprotein kinase,” Mol. Cell. Biol. 11 (4):2057-2065, Pritchard, M. A. etal., 1992, “Two members of the JAK family of protein tyrosine kinasesmap to chromosomes 1p31.3 and 9p24,” Mamm. Genome 3 (1):36-38, Witthuhn,B. A. et al., 1993, “JAK2 associates with the erythropoietin receptorand is tyrosine phosphorylated and activated following stimulation witherythropoietin,” Cell 74 (2):227-236, each of which is incorporated byreference herein in its entirety.

The nucleotide sequence of LDLR (identified by accession no. NM_000527)is disclosed in, e.g., Brown, M. S. et al., 1979, “Receptor-mediatedendocytosis: insights from the lipoprotein receptor system,” Proc. Natl.Acad. Sci. U.S.A. 76 (7):3330-3337, Goldstein, J. L. et al., 1982,“Receptor-mediated endocytosis and the cellular uptake of low densitylipoprotein,” Ciba Found. Symp. 92, 77-95, Tolleshaug H. et al., 1983,“The LDL receptor locus in familial hypercholesterolemia: multiplemutations disrupt transport and processing of a membrane receptor,” Cell32 (3):941-951, and the amino acid sequence of LDLR (identified byaccession no. NP_000518) is disclosed in, e.g., Brown, M. S. et al.,1979, “Receptor-mediated endocytosis: insights from the lipoproteinreceptor system,” Proc. Natl. Acad. Sci. U.S.A. 76 (7):3330-3337,Goldstein, J. L. et al., 1982, “Receptor-mediated endocytosis and thecellular uptake of low density lipoprotein,” Ciba Found. Symp. 92,77-95, Tolleshaug, H. et al., 1983, “The LDL receptor locus in familialhypercholesterolemia: multiple mutations disrupt transport andprocessing of a membrane receptor,” Cell 32 (3):941-951, each of whichis incorporated by reference herein in its entirety.

The nucleotide sequence of LY96 (identified by accession no. NM_015364)is disclosed in, e.g., Shimazu, R. et al., 1999, “MD-2, a molecule thatconfers lipopolysaccharide responsiveness on Toll-like receptor 4,” J.Exp. Med. 189 (11):1777-1782, Kato, K. et al., 2000, “ESOP-1, a secretedprotein expressed in the hematopoietic, nervous, and reproductivesystems of embryonic and adult mice,” Blood 96 (1):362-364, Dziarski, R.et al., 2001, “MD-2 enables Toll-like receptor 2 (TLR2)-mediatedresponses to lipopolysaccharide and enhances TLR2-mediated responses toGram-positive and Gram-negative bacteria and their cell wallcomponents,” J. Immunol. 166 (3):1938-1944, and the amino acid sequenceof LY96 (identified by accession no. NP_056179) is disclosed in, e.g.,Shimazu, R. et al., 1999, “MD-2, a molecule that conferslipopolysaccharide responsiveness on Toll-like receptor 4,” J. Exp. Med.189 (11):1777-1782, Kato, K. et al., 2000, “ESOP-1, a secreted proteinexpressed in the hematopoietic, nervous, and reproductive systems ofembryonic and adult mice,” Blood 96 (1):362-364, Dziarski, R. et al.,2001, “MD-2 enables Toll-like receptor 2 (TLR2)-mediated responses tolipopolysaccharide and enhances TLR2-mediated responses to Gram-positiveand Gram-negative bacteria and their cell wall components,” J. Immunol.166 (3):1938-1944, each of which is incorporated by reference herein inits entirety.

The nucleotide sequence of MAP2K6 (identified by accession nos.NM_002758, NM_031988) is disclosed in, e.g., Han, J. et al., 1996,“Characterization of the structure and function of a novel MAP kinasekinase (MKK6), J. Biol. Chem. 271 (6):2886-2891, Raingeaud, J. et al.,1996, “MKK3- and MKK6-regulated gene expression is mediated by the p38mitogen-activated protein kinase signal transduction pathway,” Mol.Cell. Biol. 16 (3), 1247-1255, Stein, B. et al., 1996, “Cloning andcharacterization of MEK6, a novel member of the mitogen-activatedprotein kinase kinase cascade,” J. Biol. Chem. 271 (19): 11427-11433,and the amino acid sequence of MAP2K6 (identified by accession nos.NP_002749, NP_114365) is disclosed in, e.g., Han, J. et al., 1996,“Characterization of the structure and function of a novel MAP kinasekinase (MKK6), J. Biol. Chem. 271 (6):2886-2891, Raingeaud, J. et al.,1996, “MKK3- and MKK6-regulated gene expression is mediated by the p38mitogen-activated protein kinase signal transduction pathway,” Mol.Cell. Biol. 16 (3), 1247-1255, Stein, B. et al., 1996, “Cloning andcharacterization of MEK6, a novel member of the mitogen-activatedprotein kinase kinase cascade,” J. Biol. Chem. 271 (19): 11427-11433,each of which is incorporated by reference herein in its entirety.

The nucleotide sequence of MAPK14 (identified by accession nos.NM_001315, NM_139012, NM_139013, NM_139014) is disclosed in, e.g.,Zhukov-Verezhnikov, N. N. et al., 1976, “Study of the heterogeneticantigens in vaccinal preparations of V. cholerae,” Biochem. Biophys.Res. Commun. 82 (8):961-962, Schultz, S. J. et al., 1993, Identificationof 21 novel human protein kinases, including 3 members of a familyrelated to the cell cycle regulator nimA of Aspergillus nidulans,” CellGrowth Differ. 4 (10):821-830, Lee, J. C. et al., 1994, “A proteinkinase involved in the regulation of inflammatory cytokinebiosynthesis,” Nature 372 (6508):739-746, and the amino acid sequence ofMAPK14 (identified by accession nos. NP_001306, NP_620581, NP_620582,NP_620583) is disclosed in, e.g., Zhukov-Verezhnikov, N. N. et al.,1976, “Study of the heterogenetic antigens in vaccinal preparations ofV. cholerae,” Biochem. Biophys. Res. Commun. 82 (8):961-962, Schultz, S.J. et al., 1993, Identification of 21 novel human protein kinases,including 3 members of a family related to the cell cycle regulator nimAof Aspergillus nidulans,” Cell Growth Differ. 4 (10):821-830, Lee, J. C.et al., 1994, “A protein kinase involved in the regulation ofinflammatory cytokine biosynthesis,” Nature 372 (6508):739-746, each ofwhich is incorporated by reference herein in its entirety.

The nucleotide sequence of Monocyte Chemoattractant Protein 1 (MCP1)(identified by accession nos. AF493698 and AF493697) is disclosed in,e.g., Shanmugasundaram et al., 2002, Virology II, National Institute ofImmunology, Aruna Asag Ali Marg, J.N.U. Campus, New Delhi 110 067,India, and the amino acid sequence of MCP1 (identified by accession no.AAQ75526) is disclosed in, e.g., Nyquist et al., 2003, directsubmission, Medicine, Inova Fairfax, 3300 Gallows Road, Falls Church,Va. 22402-3300, each of which is incorporated by reference herein in itsentirety.

The nucleotide sequence of MKNK1 (identified by accession nos.NM_003684, NM_198973) is disclosed in, e.g., Fukunaga et al., 1997,“MNK1, a new MAP kinase-activated protein kinase, isolated by a novelexpression screening method for identifying protein kinase substrates,EMBO J. 16: 1921-1933; Pyronnet et al., 1999, “Human eukaryotictranslation initiation factor 4G (eIF4G) recruits mnk1 to phosphorylateeIF4E,” EMBO J. 18: 270-279; Cuesta et al., 2000, “Chaperone hsp27inhibits translation during heat shock by binding eIF4G and facilitatingdissociation of cap-initiation complexes,” Genes Dev. 14: 1460-1470, andthe amino acid sequence of MKNK1 (identified by accession nos.NP_003675, NP_945324) is disclosed in, e.g., Fukunaga et al., 1997,“MNK1, a new MAP kinase-activated protein kinase, isolated by a novelexpression screening method for identifying protein kinase substrates,”EMBO J. 16:1921-1933, Pyronnet et al., 1999, “Human eukaryotictranslation initiation factor 4G (eIF4G) recruits mnk1 to phosphorylateeIF4E,” EMBO J. 18: 270-279, Cuesta et al., 2000, “Chaperone hsp27inhibits translation during heat shock by binding eIF4G and facilitatingdissociation of cap-initiation complexes,” Genes Dev. 14: 1460-1470,each of which is incorporated by reference herein in its entirety.

The nucleotide sequence of MMSP9 (identified by accession no. NM_004994)is disclosed in, e.g., Wilhelm et al., 1989, “SV40-transformed humanlung fibroblasts secrete a 92-kDa type IV collagenase which is identicalto that secreted by normal human macrophages,” J. Biol. Chem. 264:17213-17221, Huhtala et al., 1990, “Completion of the primary structureof the human type IV collagenase preproenzyme and assignment of the gene(CLG4) to the q21 region of chromosome 16,” Genomics 6: 554-559, Collieret al., 1991, “On the structure and chromosome location of the 72- and92-kDa human type IV collagenase genes,” Genomics 9: 429-434, and theamino acid sequence of MMP9 (identified by accession no. NP_004985) isdisclosed in, e.g., Wilhelm et al., 1989, “SV40-transformed human lungfibroblasts secrete a 92-kDa type IV collagenase which is identical tothat secreted by normal human macrophages,” J. Biol. Chem. 264:17213-17221, Huhtala et al., 1990, “Completion of the primary structureof the human type IV collagenase preproenzyme and assignment of the gene(CLG4) to the q21 region of chromosome 16,” Genomics 6: 554-559, Collieret al., 1991, “On the structure and chromosome location of the 72- and92-kDa human type IV collagenase genes,” Genomics 9: 429-434, each ofwhich is incorporated by reference herein in its entirety.

The nucleotide sequence of NCR1 (identified by accession no. NM_004829)is disclosed in, e.g., Sivori et al., 1997, “p46, a novel natural killercell-specific surface molecule that mediates cell activation,” J. Exp.Med. 186:1129-1136, Vitale, M. et al., NKp44, 1998, “NKp44, a noveltriggering surface molecule specifically expressed by activated naturalkiller cells, is involved in non-major histocompatibilitycomplex-restricted tumor cell lysis,” J. Exp. Med. 187: 2065-2072,Pessino et al., 1998, “Molecular cloning of NKp46: a novel member of theimmunoglobulin superfamily involved in triggering of naturalcytotoxicity,” J. Exp. Med. 188: 953-960, and the amino acid sequence ofNCR1 (identified by accession no. NP_004820) is disclosed in, e.g.,Sivori et al., 1997, “p46, a novel natural killer cell-specific surfacemolecule that mediates cell activation,” J. Exp. Med. 186:1129-1136,Vitale et al., NKp44, 1998, “NKp44, a novel triggering surface moleculespecifically expressed by activated natural killer cells, is involved innon-major histocompatibility complex-restricted tumor cell lysis,” J.Exp. Med. 187: 2065-2072, Pessino et al., 1998, “Molecular cloning ofNKp46: a novel member of the immunoglobulin superfamily involved intriggering of natural cytotoxicity,” J. Exp. Med. 188: 953-96, each ofwhich is incorporated by reference herein in its entirety.

The nucleotide sequence of OSM (identified by accession no. NM_020530)is disclosed in, e.g., Zarling et al., 1986, “Oncostatin M: a growthregulator produced by differentiated histiocytic lymphoma cells,” Proc.Natl. Acad. Sci. U.S.A. 83 (24):9739-9743, Malik et al., 1989,“Molecular cloning, sequence analysis, and functional expression of anovel growth regulator, oncostatin M,” Mol. Cell. Biol. 9 (7):2847-2853,Linsley, P. S. et al., 1990, “Cleavage of a hydrophilic C-terminaldomain increases growth-inhibitory activity of oncostatin M,” Mol. Cell.Biol. 10 (5):1882-1890, and the amino acid sequence of OSM (identifiedby accession no. NP_065391) is disclosed in, e.g., Zarling, J. M. etal., 1986, “Oncostatin M: a growth regulator produced by differentiatedhistiocytic lymphoma cells,” Proc. Natl. Acad. Sci. U.S.A. 83(24):9739-9743, Malik, N. et al., 1989, “Molecular cloning, sequenceanalysis, and functional expression of a novel growth regulator,oncostatin M,” Mol. Cell. Biol. 9 (7):2847-2853, Linsley, P. S. et al.,1990, “Cleavage of a hydrophilic C-terminal domain increasesgrowth-inhibitory activity of oncostatin M,” Mol. Cell. Biol. 10(5):1882-1890, each of which is incorporated by reference herein in itsentirety.

The nucleotide sequence of PFKFB3 (identified by accession no.NM_004566) is disclosed in, e.g., Sakai, A. et al., 1996, “Cloning ofcDNA encoding for a novel isozyme of fructose 6-phosphate,2-kinase/fructose 2,6-bisphosphatase from human placenta,” J. Biochem.119 (3):506-511, Hamilton, J. A. et al., 1997, “Identification of PRG1,a novel progestin-responsive gene with sequence homology to6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase,”

Mol. Endocrinol. 11 (4):490-502, Nicholl, J. et al., “The third humanisoform of 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase (PFKFB3)map position 10p14-p15, Chromosome Res. 5 (2):150, and the amino acidsequence of PFKFB3 (identified by accession no. NP_004557) is disclosedin, e.g., Sakai, A. et al., 1996, “Cloning of cDNA encoding for a novelisozyme of fructose 6-phosphate, 2-kinase/fructose 2,6-bisphosphatasefrom human placenta,” J. Biochem. 119 (3):506-511, Hamilton, J. A. etal., 1997, “Identification of PRG1, a novel progestin-responsive genewith sequence homology to6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase,” Mol. Endocrinol.11 (4):490-502, Nicholl, J. et al., “The third human isoform of6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase (PFKFB3) mapposition 10p14-p15, Chromosome Res. 5 (2):150, each of which isincorporated by reference herein in its entirety.

The nucleotide sequence of PRV1 (identified by accession no. NM_020406)is disclosed in, e.g., Lalezari, P. et al., 1971, “NB1, a newneutrophil-specific antigen involved in the pathogenesis of neonatalneutropenia,” J. Clin. Invest. 50 (5):1108-1115, Goldschmeding, R. etal., 1992, “Further characterization of the NB 1 antigen as a variablyexpressed 56-62 kD GPI-linked glycoprotein of plasma membranes andspecific granules of neutrophils,” Br. J. Haematol. 81 (3):336-345,Stroncek, D. F. et al., “Neutrophil-specific antigen NB1 inhibitsneutrophil-endothelial cell interactions,” J. Lab. Clin. Med. 123(2):247-255, and the amino acid sequence of PRV1 (identified byaccession no. NP_065139) is disclosed in, e.g., Lalezari, P. et al.,1971, “NB1, a new neutrophil-specific antigen involved in thepathogenesis of neonatal neutropenia,” J. Clin. Invest. 50(5):1108-1115, Goldschmeding, R. et al., 1992, “Further characterizationof the NB 1 antigen as a variably expressed 56-62 kD GPI-linkedglycoprotein of plasma membranes and specific granules of neutrophils,”Br. J. Haematol. 81 (3):336-345, Stroncek, D. F. et al.,“Neutrophil-specific antigen NB1 inhibits neutrophil-endothelial cellinteractions,” J. Lab. Clin. Med. 123 (2):247-255, each of which isincorporated by reference herein in its entirety.

The nucleotide sequence of PSTPIP2 (identified by accession no.NM_024430) is disclosed in, e.g., Hillier, L. D. et al., 1996,“Generation and analysis of 280,000 human expressed sequence tags,”Genome Res. 6 (9):807-828, Wu, Y. et al., 1998, “PSTPIP 2, a secondtyrosine phosphorylated, cytoskeletal-associated protein that binds aPEST-type protein-tyrosine phosphatase,” J. Biol. Chem. 273(46):30487-30496, Yeung, Y. G. et al., 1998, “A novel macrophageactin-associated protein (MAYP) is tyrosine-phosphorylated followingcolony stimulating factor-1 stimulation,” J. Biol. Chem. 273 (46):30638-30642, and the amino acid sequence of PSTPIP2 (identified byaccession no. NP_077748) is disclosed in, e.g., Hillier, L. D. et al.,1996, “Generation and analysis of 280,000 human expressed sequencetags,” Genome Res. 6 (9):807-828, Wu, Y. et al., 1998, “PSTPIP 2, asecond tyrosine phosphorylated, cytoskeletal-associated protein thatbinds a PEST-type protein-tyrosine phosphatase,” J. Biol. Chem. 273(46):30487-30496, Yeung, Y. G. et al., 1998, “A novel macrophageactin-associated protein (MAYP) is tyrosine-phosphorylated followingcolony stimulating factor-1 stimulation,” J. Biol. Chem. 273 (46):30638-30642, each of which is incorporated by reference herein in itsentirety.

The nucleotide sequence of SOCS3 (identified by accession no. NM_003955)is disclosed in, e.g., Minamoto, S. et al., 1997, “Cloning andfunctional analysis of new members of STAT induced STAT inhibitor (SSI)family: SSI-2 and SSI-3,” Biochem. Biophys. Res. Commun. 237 (1):79-83,Masuhara, M. et al., 1997, “Cloning and characterization of novel CISfamily genes,” Biochem. Biophys. Res. Commun. 239 (2):439-446, Zhang, J.G. et al., 1999, “The conserved SOCS box motif in suppressors ofcytokine signaling binds to elongins B and C and may couple boundproteins to proteasomal degradation,” Proc. Natl. Acad. Sci. U.S.A. 96(5):2071-2076, and the amino acid sequence of SOCS3 (identified byaccession no. NP_003946) is disclosed in, e.g., Minamoto, S. et al.,1997, “Cloning and functional analysis of new members of STAT inducedSTAT inhibitor (SSI) family: SSI-2 and SSI-3,” Biochem. Biophys. Res.Commun. 237 (1):79-83, Masuhara, M. et al., 1997, “Cloning andcharacterization of novel CIS family genes,” Biochem. Biophys. Res.Commun. 239 (2):439-446, Zhang, J. G. et al., 1999, “The conserved SOCSbox motif in suppressors of cytokine signaling binds to elongins B and Cand may couple bound proteins to proteasomal degradation,” Proc. Natl.Acad. Sci. U.S.A. 96 (5):2071-2076, each of which is incorporated byreference herein in its entirety.

The nucleotide sequence of SOD2 (identified by accession no. NM_000636)is disclosed in, e.g., Smith, M. et al., 1978, “Regional localization ofHLA, IVIES, and SODM on chromosome 6,” Cytogenet. Cell Genet. 22(1-6):428-433, Beck, Y. et al., 1987, “Human Mn superoxide dismutasecDNA sequence,” Nucleic Acids Res. 15 (21):9076, Ho, Y. S. et al., 1988,“Isolation and characterization of complementary DNAs encoding humanmanganese-containing superoxide dismutase,” FEBS Lett. 229 (2):256-260,and the amino acid sequence of SOD2 (identified by accession no.NP_000627) is disclosed in, e.g., Smith, M. et al., 1978, “Regionallocalization of HLA, IVIES, and SODM on chromosome 6,” Cytogenet. CellGenet. 22 (1-6):428-433, Beck, Y. et al., 1987, “Human Mn superoxidedismutase cDNA sequence,” Nucleic Acids Res. 15 (21):9076, Ho, Y. S. etal., 1988, “Isolation and characterization of complementary DNAsencoding human manganese-containing superoxide dismutase,” FEBS Lett.229 (2):256-260, each of which is incorporated by reference herein inits entirety.

The nucleotide sequence of TDRD9 (identified by accession no. NM_153046)is disclosed in, e.g., Isogai et al., 2003, “Homo sapiens cDNA FLJ43990fis, clone TESTI4019566, weakly similar to Dosage compensationregulator,” unpublished, and the amino acid sequence of TDRD9(identified by accession no. NP_694591) is disclosed in, e.g., Isogai etal., 2003, “Homo sapiens cDNA FLJ43990 fis, clone TESTI4019566, weaklysimilar to Dosage compensation regulator,” unpublished, each of which isincorporated by reference herein in its entirety.

The nucleotide sequence of TGFBI (identified by accession no. NM_000358)is disclosed in, e.g., Skonier et al., 1992, “cDNA cloning and sequenceanalysis of beta ig-h3, a novel gene induced in a human adenocarcinomacell line after treatment with transforming growth factor-beta,” DNACell Biol. 11 (7):511-522, Stone et al., 1994, “Three autosomal dominantcorneal dystrophies map to chromosome 5q,” Nat. Genet. 6 (1):47-51,Skonier et al., 1994, “beta ig-h3: a transforming growthfactor-beta-responsive gene encoding a secreted protein that inhibitscell attachment in vitro and suppresses the growth of CHO cells in nudemice,” DNA Cell Biol. 13 (6):571-584, and the amino acid sequence ofTGFBI (identified by accession no. NP_000349) is disclosed in, e.g.,Skonier et al., 1992, “cDNA cloning and sequence analysis of beta ig-h3,a novel gene induced in a human adenocarcinoma cell line after treatmentwith transforming growth factor-beta,” DNA Cell Biol. 11 (7):511-522;Stone et al., 1994, “Three autosomal dominant corneal dystrophies map tochromosome 5q,” Nat. Genet. 6 (1):47-51; Skonier et al., 1994, “betaig-h3: a transforming growth factor-beta-responsive gene encoding asecreted protein that inhibits cell attachment in vitro and suppressesthe growth of CHO cells in nude mice,” DNA Cell Biol. 13: 571-584, eachof which is incorporated by reference herein in its entirety.

The nucleotide sequence of TIFA (identified by accession no. NM_052864)is disclosed in, e.g., Kanamori, M. et al., 2002, “T2BP, a novel TRAF2binding protein, can activate NF-kappaB and AP-1 without TNFstimulation,” Biochem. Biophys. Res. Commun. 290 (3):1108-1113,Takatsuna, H. et al., 2003, “Identification of TIFA as an adapterprotein that links tumor necrosis factor receptor-associated factor 6(TRAF6) to interleukin-1 (IL-1) receptor-associated kinase-1 (IRAK-1) inIL-1 receptor signaling,” J. Biol. Chem. 278 (14):12144-12150, Matsudaet al., 2003, “Large-scale identification and characterization of humangenes that activate NF-kappaB and MAPK signaling pathways,” Oncogene 22(21):3307-3318, and the amino acid sequence of TIFA (identified byaccession no. NP_443096) is disclosed in, e.g., Kanamori et al., 2002,“T2BP, a novel TRAF2 binding protein, can activate NF-kappaB and AP-1without TNF stimulation,” Biochem. Biophys. Res. Commun. 290:1108-1113,Takatsuna et al., 2003, “Identification of TIFA as an adapter proteinthat links tumor necrosis factor receptor-associated factor 6 (TRAF6) tointerleukin-1 (IL-1) receptor-associated kinase-1 (IRAK-1) in IL-1receptor signaling,” J. Biol. Chem. 278 (14):12144-12150, Matsuda etal., 2003, “Large-scale identification and characterization of humangenes that activate NF-kappaB and MAPK signaling pathways,” Oncogene 22(21):3307-3318, each of which is incorporated by reference herein in itsentirety.

The nucleotide sequence of Tissue Inhibitor of Metalloproteinase 1(TIMP1) (identified by accession no. NM_003254) is disclosed in, e.g.,Domeij et al., 2005, “ell expression of MMP-1 and TIMP-1 in co-culturesof human gingival fibroblasts and monocytes: the involvement of ICAM-1,”Biochem. Biophys. Res. Commun. 338, 1825-1833; Zureik et al., “Serumtissue inhibitors of metalloproteinases 1 (TIMP-1) and carotidatherosclerosis and aortic arterial stiffness”, J. Hypertens. 23,2263-2268; Crombez, 2005, “High level production of secreted proteins:example of the human tissue inhibitor of metalloproteinases 1”, Biochem.Biophys. Res. Commun. 337, 908-915 and the amino acid sequence of TIMP1(identified by accession no. AAA75558) is disclosed in, e.g., Hardcastleet al., 1997, “Genomic organization of the human TIMP-1 gene.Investigation of a causative role in the pathogenesis of X-linkedretinitis pigmentosa,” Invest. Ophthalmol. Vis. Sci. 38, 1893-1896,which is incorporated by reference herein in its entirety.

The nucleotide sequence of TLR4 (identified by accession no. AH009665)is disclosed in, e.g., Arbour, N. C. et al., 1999, Direct Submission,Medicine, University of Iowa, 2182 Med Labs, Iowa City, Iowa 52242, USA,Arbour, N.C. et al., A Genetic Basis for a Blunted Response to Endotoxinin Humans, Arbour, N. C. et al., unpublished, “A Genetic Basis for aBlunted Response to Endotoxin in Humans”, and the amino acid sequence ofTLR4 (identified by accession no. AAF05316) is disclosed in, e.g.,Beutler, 1999, Direct Submission, Department of Internal Medicine,University of Texas Southwestern Medical Center and the Howard HughesMedical Institute, 5323 Harry Hines Boulevard, Dallas, Tex. 75235-9050,USA, Smirnova, I. et al., 2000, “Phylogenetic variation and polymorphismat the toll-like receptor 4 locus (TLR4),” Genome Biol. 1, res.002.1-002.10, each of which is incorporated by reference herein in itsentirety.

The nucleotide sequence of TNFRSF6 (identified by accession no.NM_152877) is disclosed in, e.g., Oehm, A. et al., 1992, “Purificationand molecular cloning of the APO-1 cell surface antigen, a member of thetumor necrosis factor/nerve growth factor receptor superfamily. Sequenceidentity with the Fas antigen,” J. Biol. Chem. 267 (15):10709-10715,Inazawa, J. et al., 1992, “Assignment of the human Fas antigen gene(Fas) to 10q24.1,” Genomics 14 (3):821-822, Cheng, J. et al., 1994,“Protection from Fas-mediated apoptosis by a soluble form of the Fasmolecule,” Science 263 (5154):1759-1762, and the amino acid sequence ofTNFRSF6 (identified by accession no. NP_000034) is disclosed in, e.g.,Oehm, A. et al., 1992, “Purification and molecular cloning of the APO-1cell surface antigen, a member of the tumor necrosis factor/nerve growthfactor receptor superfamily. Sequence identity with the Fas antigen,” J.Biol. Chem. 267 (15):10709-10715, Inazawa, J. et al., 1992, “Assignmentof the human Fas antigen gene (Fas) to 10q24.1,” Genomics 14(3):821-822, Cheng, J. et al., 1994, “Protection from Fas-mediatedapoptosis by a soluble form of the Fas molecule,” Science 263(5154):1759-1762, each of which is incorporated by reference herein inits entirety.

The nucleotide sequence of TNFSF10 (identified by accession no.NM_003810) is disclosed in, e.g., Wiley, S. R. et al., 1995,“Identification and characterization of a new member of the TNF familythat induces apoptosis,” Immunity 3 (6):673-682, Pitti, R. M. et al.,1996, “Induction of apoptosis by Apo-2 ligand, a new member of the tumornecrosis factor cytokine family,” J. Biol. Chem. 271 (22):12687-12690,Pan, G. et al., 1997, “The receptor for the cytotoxic ligand TRAIL,”Science 276 (5309):111-113, and the amino acid sequence of TNFSF10(identified by accession no. NP_003801) is disclosed in, e.g., Wiley, S.R. et al., 1995, “Identification and characterization of a new member ofthe TNF family that induces apoptosis,” Immunity 3 (6):673-682, Pitti,R. M. et al., 1996, “Induction of apoptosis by Apo-2 ligand, a newmember of the tumor necrosis factor cytokine family,” J. Biol. Chem. 271(22):12687-12690, Pan, G. et al., 1997, “The receptor for the cytotoxicligand TRAIL,” Science 276 (5309):111-113, each of which is incorporatedby reference herein in its entirety.

The nucleotide sequence of TNFSF13B (identified by accession no.NM_006573) is disclosed in, e.g., Shu, H. B. et al., 1999, “TALL-1 is anovel member of the TNF family that is down-regulated by mitogens,” J.Leukoc. Biol. 65 (5): 680-683, Mukhopadhyay, A. et al., 1999,“Identification and characterization of a novel cytokine, THANK, a TNFhomologue that activates apoptosis, nuclear factor-kappaB, and c-JunNH2-terminal kinase,” J. Biol. Chem. 274 (23):15978-15981, Schneider, P.et al., 1999, “BAFF, a novel ligand of the tumor necrosis factor family,stimulates B cell growth,” J. Exp. Med. 189 (11):1747-1756, and theamino acid sequence of TNFSF13B (identified by accession no. NP_006564)is disclosed in, e.g., Shu, H. B. et al., 1999, “TALL-1 is a novelmember of the TNF family that is down-regulated by mitogens,” J. Leukoc.Biol. 65 (5): 680-683, Mukhopadhyay, A. et al., 1999, “Identificationand characterization of a novel cytokine, THANK, a TNF homologue thatactivates apoptosis, nuclear factor-kappaB, and c-Jun NH2-terminalkinase,” J. Biol. Chem. 274 (23):15978-15981, Schneider, P. et al.,1999, “BAFF, a novel ligand of the tumor necrosis factor family,stimulates B cell growth,” J. Exp. Med. 189 (11):1747-1756, each ofwhich is incorporated by reference herein in its entirety.

The nucleotide sequence of VNN1 (identified by accession no. NM_004666)is disclosed in, e.g., Aurrand-Lions, M. et al., 1996, “Vanin-1, a novelGPI-linked perivascular molecule involved in thymus homing,” Immunity 5(5):391-405, Galland, F. et al., 1998, “Two human genes related tomurine vanin-1 are located on the long arm of human chromosome 6,”Genomics 53 (2):203-213, Maras, B. et al., 1999, “Is pantetheinase theactual identity of mouse and human vanin-1 proteins?,” FEBS Lett. 461(3):149-152, and the amino acid sequence of VNN1 (identified byaccession no. NP_004657) is disclosed in, e.g., Aurrand-Lions, M. etal., 1996, “Vanin-1, a novel GPI-linked perivascular molecule involvedin thymus homing,” Immunity 5 (5):391-405, Galland, F. et al., 1998,“Two human genes related to murine vanin-1 are located on the long armof human chromosome 6,” Genomics 53 (2):203-213, Maras, B. et al., 1999,“Is pantetheinase the actual identity of mouse and human vanin-1proteins?,” FEBS Lett. 461 (3):149-152, each of which is incorporated byreference herein in its entirety.

5.11.2 Exemplary Combinations of Biomarkers in Accordance withEmbodiments of the Invention

In some embodiments, the methods or kits respectively described orreferenced in Section 5.2 and Section 5.3 use at least 2, 3, 4, 5, 6, 7,8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25,30, 35, 40, 45, 50, or more biomarkers selected from Table I regardlessof whether each such biomarker has an “N” designation or a “P”designation in Table I. In some nonlimiting exemplary embodiments,between 2 and 53, between 3 and 40, between 4 and 30, or between 5 and20 such biomarkers are used.

Nucleic acid based kits and methods. In some embodiments, the methods orkits respectively described or referenced in Section 5.2 and Section 5.3use at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,19, 20, 21, 22, 23, 24, 25, 30, 35, 40, or more biomarkers selected fromTable J. Typically, in these embodiments, each biomarker is a nucleicacid (e.g., DNA, such as cDNA or amplified DNA, or RNA, such as mRNA),or a discriminating molecule or discriminating fragment of a nucleicacid. In some nonlimiting exemplary embodiments, between 2 and 44,between 3 and 35, between 4 and 25, or between 5 and 20 such biomarkersare used.

Protein or Peptide Based Kits and Methods.

In some embodiments, the methods or kits respectively described orreferenced in Section 5.2 and Section 5.3 use at least 2, 3, 4, 5, 6, 7,8, 9, or 10 of the biomarkers selected from Table K. Typically, suchbiomarkers are peptide-based (e.g., a peptide, a full length protein,etc.), or a discriminating molecule or discriminating fragment of theforegoing. In some embodiments, the biomarkers in the kit are specificantibodies to two or more of the biomarkers listed in Table K. In somenonlimiting exemplary embodiments, between 2 and 10, between 3 and 10,between 4 and 10, or between 5 and 10 such biomarkers are used.

Homogenous Kits and Methods.

In some embodiments, each of the biomarkers in the methods or kitsrespectively described or referenced in Section 5.2 and Section 5.3 useat least two or more biomarkers selected from Table I where eachbiomarker used in such methods or kits is in the same physical form. Inone example in accordance with such embodiments, each biomarker in amethod or kit in accordance Section 5.2 and Section 5.3, respectively,is a biomarker selected from Table I and is a nucleic acid or adiscriminating molecule of a nucleic acid in the method or kit. Inanother example in accordance with such embodiments, each biomarker in amethod or kit in accordance Section 5.2 and Section 5.3, respectively,is a biomarker selected from Table I and is peptide-based (e.g., apeptide, a full length protein, etc.) or a discriminating molecule ofthe forgoing. In these embodiments, biomarkers are selected withoutregard as to whether they are designated “P” or “N” in Table I. Thus, akit in accordance with these embodiments can include a biomarker innucleic acid form, even when the biomarker is designated “P” on Table I.Correspondingly, a kit in accordance with this embodiment can include abiomarker in peptidic form, even when the biomarker is designated “N” onTable I.

Heterogeneous Kits and Methods.

In some embodiments, each of the biomarkers in the methods and kitsrespectively described or referenced in Section 5.2 and Section 5.3 useat least two or more biomarkers selected from Table I where each suchbiomarker is in the same physical form that the biomarker was in whenidentified in Sections 6.11 through 6.13 below. In other words, if thebiomarker has an “N” designation in Table I, a nucleic acid form of thebiomarker is used in the methods and kits respectively described orreferenced in Section 5.2 and 5.3 in accordance with this embodiment ofthe invention. If the biomarker has a “P” designation in Table I, apeptidic form of the biomarker is used in the methods and kitsrespectively described or referenced in Section 5.2 and 5.3 inaccordance with this embodiment of the invention. Further, there is atleast one biomarker used in such methods or kits that has an “N”designation in Table I and at least one biomarker that has a “P”designation. In such embodiments, biomarkers having an N designation inTable I are nucleic acids and biomarkers having a P designation in TableI are peptide-based or protein-based.

A non-limiting exemplary kit in accordance with such mixed embodimentsuse two biomarkers from among the biomarkers listed in Table J, innucleic acid form, and three biomarkers from among the biomarkers listedin Table K, in peptidic-based form. In some embodiments, thenon-limiting methods and kits respectively described or referenced inSections 5.2 and 5.3 use at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 30, 35, 40, or morebiomarkers from Table J, in nucleic acid form, and 1, 2, 3, 4, 5, 6, 7,8, 9, or 10 biomarkers from Table K in peptide-based or protein-basedform.

Additional Kits and Methods.

In some embodiments, each of the biomarkers in the methods and kitsrespectively described or referenced in Section 5.2 and Section 5.3 useat least one biomarkers selected from Table I and at least one differentbiomarker from Table 31. In some embodiments, each of the biomarkers inthe methods and kits respectively described or referenced in Section 5.2and Section 5.3 use at least 2, 3, 4, 5, 6, 7, 8, 9, or 10 biomarkersselected from Table I and at least 2, 3, 4, 5, 6, 7, 8, 9, or 10different biomarkers from Table 31.

In some embodiments, each of the biomarkers in the methods and kitsrespectively described or referenced in Section 5.2 and Section 5.3 useat least one biomarker in, nucleic acid form, selected from Table J andat least one different biomarker from Table 31. In some embodiments,each of the biomarkers in the methods and kits respectively described orreferenced in Section 5.2 and Section 5.3 use at least 2, 3, 4, 5, 6, 7,8, 9, or 10 biomarkers selected from Table I, each in nucleic acid form,and at least 2, 3, 4, 5, 6, 7, 8, 9, or 10 different biomarkers fromTable 31.

In some embodiments, each of the biomarkers in the methods and kitsrespectively described or referenced in Section 5.2 and Section 5.3 useat least one biomarker in, protein form, selected from Table K and atleast one different biomarker from Table 31. In some embodiments, eachof the biomarkers in the methods and kits respectively described orreferenced in Section 5.2 and Section 5.3 use at least 2, 3, 4, 5, 6, 7,8, 9, or 10 biomarkers selected from Table I, each in protein form, andat least 2, 3, 4, 5, 6, 7, 8, 9, or 10 different biomarkers from Table31.

In some embodiments, each of the biomarkers in the methods and kitsrespectively described or referenced in Section 5.2 and Section 5.3 useat least one biomarker from among the biomarkers listed in Table J, innucleic acid form, and at least one biomarkers from among the biomarkerslisted in Table K, in protein form. In some embodiments, thenon-limiting methods and kits respectively described or referenced inSections 5.2 and 5.3 use at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 30, 35, 40, or morebiomarkers from Table J, in nucleic acid form, and 1, 2, 3, 4, 5, 6, 7,8, 9, or 10 biomarkers from Table K in protein form.

In some embodiments, any of the above-described combinations ofbiomarkers are used in methods or kits in accordance Section 5.2 andSection 5.3 with the exception that the IL-6, IL-8, MMP9, B2M, HLA-DRA,and MCP1 biomarkers are not used in such methods or kits. For example,in embodiments where certain monocytes are isolated from whole blood andtested, such biomarkers are not utilized, especially when suchbiomarkers are nucleic acids. In some embodiments, any of theabove-described combinations of biomarkers are used in methods or kitsin accordance Section 5.2 and Section 5.3 with the exception that theIL-6, IL-8, IL-10, and CRP protein biomarkers are not used in suchmethods or kits. In some embodiments, any of the above-describedcombinations of biomarkers are used in methods or kits in accordanceSection 5.2 and Section 5.3 with the exception that the IL-6, IL-8,IL-10, and CRP nucleic acid biomarkers are not used in such methods orkits. In some embodiments, any of the above-described combinations ofbiomarkers are used in methods or kits in accordance Section 5.2 andSection 5.3 with the exception that the IL-6 and MAPK biomarkers are notused in such methods or kits. In some embodiments, any of theabove-described combinations of biomarkers are used in methods or kitsin accordance Section 5.2 and Section 5.3 with the exception that theIL-6, IL-8, and IL-10 biomarkers are not used in such methods or kits.In some embodiments, any of the above-described combinations ofbiomarkers are used in methods or kits in accordance Section 5.2 andSection 5.3 with the exception that the CD86, IL-6, IL-8, IL-10, and CRPbiomarkers are not used in such methods or kits. In some embodiments,any of the above-described combinations of biomarkers are used inmethods or kits in accordance Section 5.2 and Section 5.3 with theexception that the IL-6 and IL-10 biomarkers are not used in suchmethods or kits. In some embodiments, any of the above-describedcombinations of biomarkers are used in methods or kits in accordanceSection 5.2 and Section 5.3 with the exception that the IL-6 and CRPbiomarkers are not used in such methods or kits. In some embodiments,any of the above-described combinations of biomarkers are used inmethods or kits in accordance Section 5.2 and Section 5.3 with theexception that the CRP biomarker is not used in such methods or kits. Insome embodiments, any of the above-described combinations of biomarkersare used in methods or kits in accordance Section 5.2 and Section 5.3with the exception that the IL-8 biomarker is not used in such methodsor kits. In some embodiments, any of the above-described combinations ofbiomarkers are used in methods or kits in accordance Section 5.2 andSection 5.3 with the exception that the B2M biomarker is not used insuch methods or kits.

5.11.3 Exemplary Subcombinations of Biomarkers in Accordance withEmbodiments of the Invention

In some embodiments, the methods or kits respectively described orreferenced in Section 5.2 and Section 5.3 use any one biomarker setlisted in Table L. The biomarker sets listed in Table L were identifiedin the computational experiments described in Section 6.14.1, below, inwhich 4600 random subcombinations of the biomarkers listed in Table Jwere tested. Table L, below, lists some of the biomarker sets thatprovided high accuracy scores against the validation populationdescribed in Section 6.14.1. Each row of Table L lists a singlebiomarker set that can be used in the methods and kits respectivelyreferenced in Sections 5.2 and 5.3. In other words, each row of Table Ldescribes a biomarker set that can be used to discriminate betweensepsis and SIRS subjects (e.g., to determine whether a subject is likelyto acquire sepsis). In some embodiments, nucleic acid forms of thebiomarkers listed in a biomarker set in Table L are used in the methodsand kits respectively referenced in Sections 5.2 and 5.3. In someembodiments, protein forms of the biomarkers listed in a biomarker setin Table L are used in the methods and kits respectively referenced inSections 5.2 and 5.3. In some hybrid embodiments, some of the biomarkersin a biomarker set listed in Table L are in protein form and some of thebiomarkers in the same biomarker set from Table L are in nucleic acidform in the methods and kits respectively referenced in Sections 5.2 and5.3.

In some embodiments, a given biomarker set listed in Table L is usedwith the addition of one, two, three, four, five, six, seven, eight, ornine or more additional biomarkers listed in Table I that are not withinthe given set of biomarkers from Table L. In some embodiments, a givenbiomarker set listed in Table L is used with the addition of one, two,three, four, five, six, seven, eight, or nine or more additionalbiomarkers from any one of Tables I, 30, 31, 32, 33, 34, or 36 that arenot within the given biomarker set from Table L. In Table L, accuracy,specificity, and senstitivity are described with reference to T⁻¹² timepoint data described in Section 6.14.1, below.

TABLE L Exemplary sets of biomarkers used in the methods or kitsreferenced in Sections 5.2 and 5.3 BIOMARKER SET ACCURACY SPECIFICITYSENSISTIVITY INSL3, BCL2A1, CD86 0.82 0.82 0.83 MAP2K6, INSL3, CD86 0.820.75 0.87 ARG2, MAP2K6, SOCS3 0.82 0.75 0.87 NCR1, GADD45A, OSM 0.810.77 0.85 GADD45B, TNFSF13B, PFKFB3 0.80 0.74 0.87 TLR4, FCGR1A, CSF1R0.80 0.82 0.78 SOCS3, FCGR1A, PSTPIP2 0.80 0.79 0.81 TGFBI, MAP2K6,PSTPIP2 0.80 0.76 0.83 IFNGR1, JAK2, TNFRSF6, OSM 0.83 0.80 0.87 IRAK2,GADD45A, CD86, JAK2 0.83 0.86 0.81 GADD45A, PRV1, OSM, FCGR1A 0.83 0.800.86 IRAK4, CCL5, INSL3, CD86 0.83 0.76 0.90 VNN1, BCL2A1, GADD45B,FAD104 0.82 0.83 0.81 OSM, CD86, PRV1, BCL2A1 0.82 0.78 0.85 VNN1,SOCS3, CSF1R, FCGR1A 0.82 0.78 0.85 VNN1, CCL5, ANKRD22, OSM 0.82 0.770.86 LDLR, SOCS3, CD86, IL10alpha 0.81 0.78 0.85 TLR4, SOCS3, IRAK2,CSF1R 0.81 0.76 0.85 IL1RN, SOCS3, ARG2, LDLR 0.81 0.76 0.84 IL18R1,MAP2K6, TGFBI, OSM 0.80 0.86 0.75 FCGR1A, HLA-DRA, IL18R1, PSTPIP2 0.800.79 0.82 OSM, IL1RN, SOD2, SOCS3 0.80 0.78 0.82 NCR1, JAK2, TNFSF13B,FCGR1A 0.80 0.76 0.86 TIFA, VNN1, ANXA3, ITGAM 0.80 0.73 0.88 PFKFB3,IRAK2, CSF1R, CD86, PSTPIP2 0.88 0.83 0.91 PSTPIP2, FAD104, TIFA, CD86,LY96 0.84 0.85 0.84 IL1RN, IL10alpha, IFNGR1, OSM, MKNK1 0.83 0.78 0.89IL18R1, CCL5, JAK2, SOCS3, SOD2 0.83 0.81 0.84 JAK2, MKNK1, TNFSF13B,PRV1, TNFSF10 0.83 0.81 0.84 MAP2K6, ARG2, OSM, ANKRD22, 0.83 0.8 0.85Gene_MMP9 SOCS3, IL1RN, ARG2, FCGR1A, CCL5 0.83 0.78 0.87 CCL5, INSL3,SOD2, TLR4, ARG2 0.83 0.78 0.87 FCGR1A, ARG2, CD86, MAPK14, TNFRSF6 0.820.83 0.82 INSL3, TLR4, SOCS3, CSF1R, FCGR1A 0.82 0.79 0.85 CEACAM1,TNFRSF6, MAPK14, IL10alpha, 0.82 0.79 0.84 CSF1R ANKRD22, CD86, CRTAP,OSM, PFKFB3 0.82 0.79 0.84 OSM, IL18R1, LDLR, GADD45B, MKNK1 0.82 0.760.86 CRTAP, SOCS3, PSTPIP2, TIFA, FAD104 0.81 0.82 0.81 PSTPIP2, ARG2,IL10alpha, TLR4, CSF1R 0.81 0.81 0.82 TIFA, PFKFB3, CSF1R, LDLR,Gene_MMP9 0.81 0.79 0.83 NCR1, PSTPIP2, GADD45A, LY96, MAPK14 0.81 0.770.86 ARG2, BCL2A1, NCR1, PSTPIP2, IL10alpha 0.81 0.82 0.8 PFKFB3, OSM,CSF1R, CD86, TIFA 0.81 0.81 0.81 IL10alpha, CD86, SOCS3, GADD45A, TGFBI0.81 0.78 0.84 PSTPIP2, PFKFB3, INSL3, PRV1, IL1RN 0.81 0.78 0.85 ITGAM,PRV1, IL18R1, INSL3, JAK2 0.81 0.77 0.85 PSTPIP2, OSM, IL18R1, TNFSF13B,ITGAM 0.81 0.72 0.9 CD86, TIFA, CSF1R, FCGR1A, CRTAP 0.81 0.84 0.78LY96, TGFBI, SOCS3, ANKRD22, MAPK14 0.81 0.83 0.79 IL1RN, SOD2, VNN1,OSM, TNFSF10 0.81 0.79 0.82 CRTAP, NCR1, OSM, PRV1, ANXA3 0.81 0.76 0.85TDRD9, LY96, CEACAM1, OSM, NCR1 0.81 0.72 0.88 TGFBI, INSL3, GADD45A,LDLR, PSTPIP2 0.8 0.75 0.85 PFKFB3, IRAK2, CSF1R, CD86, PSTPIP2 0.880.83 0.91 MAP2K6, ARG2, CD86, PRV1, FAD104, 0.85 0.86 0.84 MAPK14 ARG2,LY96, INSL3, MAP2K6, TNFSF10, 0.85 0.86 0.84 NCR1 SOCS3, GADD45B, CSF1R,ARG2, PSTPIP2, 0.85 0.83 0.86 OSM GADD45B, PFKFB3, PSTPIP2, FCGR1A, 0.850.81 0.88 HLA-DRA, ARG2 TIFA, IL18R1, MAPK14, CD86, ARG2, 0.84 0.8 0.88TNFSF13B FCGR1A, ARG2, GADD45B, IL10alpha, 0.84 0.81 0.86 NCR1, LDLRTGFBI, INSL3, IRAK4, GADD45B, SOCS3, 0.84 0.8 0.87 CSF1R SOCS3, CSF1R,CEACAM1, ARG2, 0.83 0.82 0.85 IL10alpha, IFNGR1 TLR4, PFKFB3, ARG2,PRV1, LDLR, 0.83 0.81 0.85 TNFSF13B PSTPIP2, OSM, TLR4, INSL3, IRAK4,0.83 0.8 0.85 IL18R1 GADD45A, CCL5, FCGR1A, PSTPIP2, 0.82 0.83 0.82MAP2K6, IL1RN OSM, FAD104, JAK2, CRTAP, TDRD9, 0.82 0.79 0.85 TNFSF13BFAD104, SOCS3, TNFSF13B, GADD45B, 0.82 0.84 0.81 CRTAP, TGFBI IL18R1,TNFRSF6, INSL3, CD86, ANXA3, 0.82 0.79 0.84 PSTPIP2 HLA-DRA, INSL3,ARG2, CD86, CCL5, 0.82 0.79 0.84 SOCS3 TNFRSF6, IL18R1, CD86, PFKFB3,0.81 0.82 0.81 IL10alpha, FAD104 FAD104, TGFBI, TDRD9, CD86, SOD2, 0.810.79 0.83 ARG2 CD86, ARG2, GADD45A, TLR4, BCL2A1, 0.81 0.79 0.83 GADD45BSOD2, CEACAM1, OSM, GADD45A, 0.81 0.74 0.88 PSTPIP2, IL10alpha FCGR1A,CSF1R, NCR1, ANXA3, SOCS3, 0.81 0.81 0.8 Gene_MMP9 TNFSF10, IL1RN, OSM,CSF1R, PSTPIP2, 0.81 0.78 0.83 JAK2 CD86, VNN1, LDLR, IL1RN, MAP2K6,0.81 0.76 0.84 TDRD9 ARG2, OSM, CSF1R, ITGAM, CRTAP, 0.81 0.76 0.85SOCS3 ANXA3, CSF1R, CEACAM1, Gene_MMP9, 0.8 0.8 0.81 CD86, OSM LY96,VNN1, SOD2, TGFBI, ARG2, CSF1R 0.8 0.78 0.83 GADD45A, PSTPIP2, BCL2A1,ANKRD22, 0.8 0.77 0.83 HLA-DRA, ANXA3 TGFBI, FCGR1A, ARG2, CD86, PFKFB3,0.86 0.86 0.85 BCL2A1, TNFRSF6 SOCS3, ITGAM, TDRD9, INSL3, PRV1, 0.840.81 0.87 TGFBI, ARG2 MKNK1, GADD45B, IRAK2, TIFA, OSM, 0.83 0.81 0.85VNN1, PSTPIP2 SOCS3, PSTPIP2, TDRD9, IL10alpha, ARG2, 0.83 0.82 0.84CD86, CCL5 CSF1R, PSTPIP2, MAPK14, INSL3, IL18R1, 0.83 0.78 0.87 JAK2,OSM MKNK1, PSTPIP2, ARG2, LY96, ANKRD22, 0.82 0.85 0.8 SOCS3, IRAK4PSTPIP2, FAD104, TNFSF13B, ITGAM, 0.82 0.83 0.82 BCL2A1, FCGR1A, ANXA3SOCS3, IRAK2, IFNGR1, CD86, OSM, 0.82 0.81 0.83 PSTPIP2, GADD45A INSL3,NCR1, PSTPIP2, PFKFB3, 0.82 0.8 0.84 ANKRD22, HLA-DRA, MKNK1 FCGR1A,HLA-DRA, CSF1R, SOCS3, 0.82 0.76 0.88 IRAK4, TIFA, ARG2 FAD104, TGFBI,MAP2K6, IRAK4, LY96, 0.82 0.81 0.83 CD86, ANKRD22 LDLR, INSL3, GADD45B,ARG2, PFKFB3, 0.82 0.78 0.86 HLA-DRA, ITGAM FCGR1A, TIFA, CD86, PFKFB3,TDRD9, 0.82 0.79 0.85 GADD45A, LDLR SOCS3, CSF1R, SOD2, CD86, MAP2K6,0.82 0.76 0.86 GADD45B, PSTPIP2 MKNK1, CD86, FAD104, PRV1, SOCS3, 0.820.76 0.87 IL10alpha, MAP2K6 TIFA, JAK2, LDLR, IRAK2, VNN1, CD86, 0.810.81 0.81 ARG2 ANKRD22, MAPK14, INSL3, BCL2A1, 0.81 0.79 0.83 CRTAP,IRAK2, FCGR1A FAD104, INSL3, CD86, TNFRSF6, 0.81 0.79 0.83 GADD45A,IFNGR1, JAK2 CSF1R, INSL3, VNN1, TIFA, IFNGR1, 0.81 0.79 0.83 LDLR, ARG2PFKFB3, BCL2A1, ANXA3, IL10alpha, 0.81 0.79 0.84 FAD104, VNN1, INSL3CSF1R, CEACAM1, MAP2K6, GADD45B, 0.81 0.82 0.8 TNFSF10, TNFSF13B, TIFAFCGR1A, ARG2, IRAK2, GADD45A, CD86, 0.81 0.82 0.79 Gene_MMP9, BCL2A1CRTAP, CEACAM1, FAD104, MKNK1, 0.81 0.76 0.84 INSL3, ITGAM, SOD2 OSM,TDRD9, BCL2A1, IRAK2, GADD45A, 0.8 0.77 0.84 CD86, LDLR PFKFB3, CCL5,CSF1R, LDLR, TLR4, LY96, 0.8 0.77 0.84 FAD104 IRAK4, GADD45B, CEACAM1,FAD104, 0.8 0.75 0.85 CSF1R, IRAK2, MAPK14 IFNGR1, FAD104, MAP2K6,TNFRSF6, 0.8 0.74 0.86 FCGR1A, IRAK2, ARG2 TGFBI, IRAK2, CRTAP, BCL2A1,ITGAM, 0.8 0.81 0.79 ANXA3, FCGR1A SOD2, PFKFB3, GADD45B, IRAK2, PRV1,0.8 0.77 0.82 SOCS3, FCGR1A TNFRSF6, TLR4, IRAK2, ITGAM, JAK2, 0.8 0.770.83 OSM, NCR1 IL10alpha, ANKRD22, Gene_MMP9, IL1RN, 0.8 0.76 0.84 LY96,FAD104, PSTPIP2 IRAK4, INSL3, CSF1R, ITGAM, VNN1, 0.8 0.74 0.86 HLA-DRA,IL18R1 IRAK2, TGFBI, MAP2K6, IL18R1, IFNGR1, 0.8 0.73 0.87 CRTAP,PSTPIP2 TDRD9, ITGAM, OSM, NCR1, CD86, 0.8 0.73 0.87 MAP2K6, CCL5 ANXA3,FCGR1A, TNFSF10, VNN1, 0.85 0.86 0.84 TNFSF13B, ARG2, 12, CD86 INSL3,PFKFB3, MAPK14, FCGR1A, 0.85 0.84 0.85 TDRD9, CSF1R, 12, IRAK4 VNN1,CSF1R, ANKRD22, OSM, 0.85 0.78 0.9 GADD45A, LY96, 12, MAP2K6 ITGAM, OSM,LY96, TDRD9, ANKRD22, 0.84 0.84 0.83 TLR4, 12, MKNK1 ARG2, ANXA3,MAP2K6, CCL5, CD86, 0.84 0.84 0.83 OSM, 12, LDLR OSM, IL1RN, FCGR1A,GADD45A, ARG2, 0.84 0.82 0.86 IL10alpha, 12, ITGAM TIFA, ANKRD22,TNFSF13B, CRTAP, 0.84 0.81 0.86 MAP2K6, IRAK4, 12, ARG2 IRAK2, TLR4,IL10alpha, TGFBI, PRV1, 0.83 0.8 0.86 FAD104, 12, MAP2K6 IL18R1, FAD104,TNFSF13B, MAP2K6, 0.83 0.81 0.85 OSM, SOD2, 12, TNFRSF6 OSM, LDLR, VNN1,LY96, ARG2, MAPK14, 0.83 0.8 0.85 12, IRAK2 PRV1, ITGAM, SOD2,Gene_MMP9, OSM, 0.83 0.79 0.86 JAK2, 12, ARG2 ANXA3, TNFSF10, CEACAM1,FCGR1A, 0.83 0.78 0.86 HLA-DRA, IL10alpha, 12, SOCS3 ITGAM, CD86,CEACAM1, TDRD9, 0.83 0.77 0.88 GADD45A, PFKFB3, 12, SOCS3 SOCS3, PRV1,ARG2, CEACAM1, LDLR, 0.82 0.83 0.82 GADD45A, 12, IL10alpha INSL3, CSF1R,IL1RN, PSTPIP2, MKNK1, 0.82 0.8 0.84 SOCS3, 12, JAK2 TDRD9, LY96, ITGAM,NCR1, PSTPIP2, 0.82 0.75 0.9 IL10alpha, 12, OSM PFKFB3, MAP2K6, ARG2,TGFBI, LDLR, 0.82 0.84 0.8 FAD104, 12, MAPK14 ARG2, IL18R1, NCR1, CD86,FCGR1A, 0.82 0.79 0.84 TGFBI, 12, IL1RN HLA-DRA, CEACAM1, IFNGR1, MKNK1,0.82 0.76 0.87 LDLR, GADD45B, 12, CSF1R IL10alpha, IL1RN, OSM, PSTPIP2,INSL3, 0.82 0.81 0.82 TIFA, 12, TLR4 MKNK1, CSF1R, VNN1, OSM, ARG2, 0.810.81 0.82 GADD45B, 12, SOCS3 IL18R1, GADD45B, TNFRSF6, TNFSF10, 0.810.79 0.84 TIFA, JAK2, 12, GADD45A LDLR, IL10alpha, PRV1, LY96, ANXA3,0.81 0.76 0.86 TNFRSF6, 12, CCL5 IL18R1, CD86, PFKFB3, ANKRD22, CSF1R,0.81 0.8 0.82 SOCS3, 12, TIFA CCL5, TDRD9, PSTPIP2, ARG2, INSL3, 0.810.77 0.85 OSM, 12, CSF1R CEACAM1, IL10alpha, IL18R1, PSTPIP2, 0.81 0.770.85 TGFBI, TIFA, 12, VNN1 GADD45B, Gene_MMP9, TLR4, PFKFB3, 0.81 0.760.85 VNN1, JAK2, 12, IL18R1 FAD104, MAPK14, IFNGR1, IL18R1, 0.81 0.760.86 TNFSF10, CD86, 12, HLA-DRA PSTPIP2, SOCS3, OSM, CSF1R, PFKFB3, 0.810.75 0.86 NCR1, 12, PRV1 TGFBI, SOCS3, ITGAM, TNFSF13B, 0.81 0.8 0.81IL18R1, PSTPIP2, 12, ANKRD22 CEACAM1, SOCS3, PFKFB3, TNFRSF6, 0.81 0.790.82 PSTPIP2, OSM, 12, BCL2A1 TNFRSF6, ITGAM, BCL2A1, INSL3, CD86, 0.810.77 0.84 TIFA, 12, PFKFB3 PFKFB3, PSTPIP2, MAP2K6, IRAK4, OSM, 0.810.76 0.85 CCL5, 12, TNFSF10 MKNK1, TIFA, IL1RN, ARG2, SOCS3, 0.81 0.760.85 IL10alpha, 12, IFNGR1 FAD104, TNFSF13B, OSM, BCL2A1, 0.8 0.8 0.8TDRD9, LY96, 12, SOD2 CD86, SOCS3, PSTPIP2, CCL5, OSM, TLR4, 0.8 0.770.83 12, MAPK14 TGFBI, LDLR, CRTAP, CSF1R, NCR1, 0.8 0.82 0.78 LY96, 12,PSTPIP2 JAK2, TIFA, TNFSF10, IL18R1, CCL5, 0.8 0.82 0.78 INSL3, 12, VNN1TIFA, IL1RN, MAP2K6, HLA-DRA, OSM, 0.8 0.8 0.8 FAD104, 12, INSL3 JAK2,IRAK2, PRV1, TNFSF13B, OSM, 0.8 0.8 0.8 HLA-DRA, 12, IFNGR1 ANXA3,CSF1R, TLR4, SOCS3, IRAK4, 0.8 0.8 0.8 PRV1, 12, INSL3 TGFBI, IRAK4,PFKFB3, SOD2, ANXA3, 0.8 0.8 0.8 ITGAM, 12, TDRD9 TIFA, FCGR1A, TNFRSF6,LY96, IL10alpha, 0.8 0.78 0.81 SOCS3, 12, OSM PRV1, TLR4, CSF1R, IL18R1,PSTPIP2, 0.8 0.78 0.82 TDRD9, 12, HLA-DRA LY96, TNFSF13B, OSM, TGFBI,TIFA, 0.8 0.77 0.82 FAD104, 12, NCR1 ITGAM, ARG2, IL10alpha, SOD2, LY96,0.8 0.76 0.83 OSM, 12, FCGR1A ANKRD22, HLA-DRA, PRV1, NCR1, 0.8 0.760.84 CSF1R, PSTPIP2, 12, LY96 Gene_MMP9, PSTPIP2, GADD45B, SOD2, 0.80.74 0.86 ANKRD22, TNFSF13B, 12, ITGAM ANXA3, FCGR1A, TNFSF10, VNN1,0.85 0.86 0.84 TNFSF13B, ARG2, 12, CD86 CSF1R, PFKFB3, BCL2A1, SOCS3,NCR1, 0.86 0.87 0.85 TNFSF10, FCGR1A, ARG2, CD86 PSTPIP2, ITGAM, IRAK2,OSM, NCR1, 0.84 0.81 0.87 CEACAM1, PFKFB3, TLR4, ANXA3 INSL3, ARG2,LDLR, HLA-DRA, NCR1, 0.84 0.78 0.9 TIFA, LY96, ITGAM, SOCS3 CD86, CSF1R,SOD2, OSM, SOCS3, 0.83 0.79 0.87 BCL2A1, GADD45B, ARG2, HLA-DRA ARG2,CD86, MAP2K6, HLA-DRA, 0.83 0.82 0.84 IL10alpha, IRAK2, GADD45B, MKNK1,IL18R1 IFNGR1, BCL2A1, ARG2, TNFSF13B, 0.83 0.8 0.86 GADD45A, FCGR1A,TNFRSF6, CD86, MAP2K6 OSM, TNFSF10, CSF1R, CCL5, IRAK2, 0.83 0.82 0.83INSL3, ARG2, TNFSF13B, TNFRSF6 LY96, TNFSF10, GADD45B, CRTAP, ARG2, 0.820.87 0.77 ANXA3, CSF1R, CCL5, OSM NCR1, LY96, FAD104, ANKRD22, BCL2A1,0.82 0.79 0.84 PSTPIP2, ARG2, PRV1, IL18R1 SOD2, IRAK2, JAK2, CCL5,IL10alpha, 0.82 0.77 0.86 ARG2, BCL2A1, SOCS3, CSF1R TNFRSF6, TGFBI,FCGR1A, IRAK4, 0.82 0.81 0.83 GADD45A, LDLR, IFNGR1, CSF1R, TIFAGADD45B, ITGAM, PRV1, SOD2, 0.81 0.79 0.83 TNFSF13B, HLA-DRA, FAD104,TNFRSF6, TLR4 IRAK2, SOCS3, GADD45B, MAP2K6, PRV1, 0.81 0.83 0.8 PFKFB3,CD86, IFNGR1, ANKRD22 HLA-DRA, GADD45A, FCGR1A, 0.81 0.81 0.81 ANKRD22,ARG2, NCR1, BCL2A1, IRAK2, SOCS3 IRAK4, SOCS3, MKNK1, JAK2, OSM, 0.810.79 0.83 ANXA3, VNN1, ITGAM, TNFRSF6 SOD2, JAK2, FAD104, CD86, ARG2,CCL5, 0.81 0.79 0.83 MAP2K6, IFNGR1, PFKFB3 IL18R1, CSF1R, IRAK2,HLA-DRA, 0.81 0.78 0.83 PFKFB3, CRTAP, CD86, TIFA, TNFSF10 MAP2K6,FAD104, TGFBI, IRAK4, CRTAP, 0.81 0.77 0.84 LDLR, IRAK2, FCGR1A, ARG2CEACAM1, SOD2, GADD45A, VNN1, 0.81 0.73 0.88 IRAK4, OSM, TDRD9, GADD45B,PSTPIP2 PSTPIP2, ANKRD22, TNFSF10, INSL3, 0.81 0.84 0.78 HLA-DRA, NCR1,TNFSF13B, CSF1R, Gene_MMP9 JAK2, MAP2K6, CSF1R, IRAK2, TNFSF10, 0.810.81 0.8 LDLR, OSM, BCL2A1, ARG2 Gene_MMP9, MAP2K6, IL18R1, VNN1, 0.810.8 0.81 INSL3, ANKRD22, CCL5, PFKFB3, MAPK14 IL18R1, ARG2, FCGR1A,CRTAP, 0.81 0.8 0.82 GADD45B, FAD104, IRAK4, MAPK14, TDRD9 SOD2, PRV1,MKNK1, FCGR1A, CD86, 0.81 0.78 0.83 GADD45A, IL18R1, TNFSF13B, HLA-DRAANXA3, TNFRSF6, MAP2K6, OSM, 0.81 0.78 0.83 ANKRD22, IL18R1, MAPK14,GADD45A, GADD45B OSM, IRAK2, ANXA3, TNFSF13B, IL18R1, 0.81 0.78 0.83ANKRD22, MAP2K6, IL10alpha, FAD104 ITGAM, SOD2, CSF1R, TGFBI, IFNGR1,0.81 0.73 0.87 TDRD9, JAK2, ARG2, GADD45A INSL3, ITGAM, OSM, TIFA,IRAK2, 0.8 0.84 0.77 MKNK1, SOCS3, TNFSF10, ANKRD22 GADD45A, PFKFB3,SOD2, IRAK2, 0.8 0.79 0.82 MAPK14, INSL3, IRAK4, ITGAM, ARG2 NCR1,INSL3, ARG2, IFNGR1, LDLR, OSM, 0.8 0.78 0.83 PRV1, GADD45B, CD86 IRAK2,FAD104, TLR4, CSF1R, PRV1, OSM, 0.8 0.77 0.83 MKNK1, BCL2A1, CD86 NCR1,SOCS3, HLA-DRA, PFKFB3, 0.8 0.74 0.86 FAD104, IRAK4, VNN1, CCL5, MAP2K6CRTAP, TLR4, PFKFB3, CSF1R, TIFA, 0.8 0.82 0.78 PSTPIP2, PRV1, IFNGR1,CCL5 LY96, SOD2, IL18R1, TNFRSF6, TLR4, 0.8 0.81 0.79 MAP2K6, FAD104,Gene_MMP9, NCR1 ITGAM, SOD2, SOCS3, LDLR, MAP2K6, 0.8 0.8 0.8 FAD104,NCR1, CSF1R, CD86 CRTAP, ARG2, SOD2, TDRD9, TNFRSF6, 0.8 0.8 0.8 TIFA,OSM, Gene_MMP9, HLA-DRA OSM, LY96, CEACAM1, IRAK4, INSL3, 0.8 0.78 0.82PSTPIP2, PRV1, IRAK2, JAK2 CD86, IL1RN, IFNGR1, ANXA3, CSF1R, 0.8 0.780.82 ITGAM, NCR1, TDRD9, MAP2K6 TNFSF13B, JAK2, IRAK4, TDRD9, HLA- 0.80.78 0.82 DRA, SOCS3, PSTPIP2, FAD104, SOD2 Gene_MMP9, SOD2, JAK2, CD86,HLA- 0.8 0.74 0.85 DRA, IRAK2, CEACAM1, MAPK14, ANXA3 GADD45B, ITGAM,TLR4, NCR1, CD86, 0.8 0.71 0.88 TNFSF13B, HLA-DRA, FCGR1A, OSM OSM,GADD45B, CSF1R, CCL5, ANXA3, 0.85 0.85 0.84 CEACAM1, CD86, TNFSF10,ARG2, LY96, TDRD9 NCR1, HLA-DRA, BCL2A1, ARG2, SOCS3, 0.84 0.84 0.84IL18R1, PSTPIP2, VNN1, CD86, GADD45A, CCL5 PFKFB3, SOCS3, TNFRSF6,GADD45A, 0.84 0.8 0.87 OSM, TDRD9, IL18R1, NCR1, CSF1R, ANXA3, PSTPIP2ARG2, IFNGR1, MAPK14, Gene_MMP9, 0.84 0.8 0.88 IRAK4, CEACAM1, ITGAM,ANKRD22, GADD45B, VNN1, OSM BCL2A1, LY96, GADD45B, IL10alpha, 0.84 0.820.86 CRTAP, OSM, IFNGR1, IL1RN, TIFA, IRAK4, GADD45A TGFBI, SOCS3,MAP2K6, ANXA3, TLR4, 0.83 0.77 0.89 IL1RN, VNN1, HLA-DRA, TIFA, JAK2,TDRD9 TNFSF13B, GADD45A, ANXA3, IL18R1, 0.83 0.83 0.83 FCGR1A, JAK2,CD86, SOCS3, INSL3, CRTAP, NCR1 LY96, INSL3, TNFSF10, MAP2K6, OSM, 0.830.78 0.88 ITGAM, JAK2, CD86, FCGR1A, IL10alpha, CCL5 ARG2, OSM, TLR4,NCR1, CCL5, BCL2A1, 0.83 0.76 0.88 IL1RN, GADD45A, MAPK14, SOCS3, TDRD9INSL3, IL18R1, IFNGR1, ARG2, IL10alpha, 0.83 0.82 0.83 LY96, CRTAP,LDLR, JAK2, CSF1R, VNN1 ANXA3, IFNGR1, GADD45A, TNFRSF6, 0.83 0.79 0.86CCL5, JAK2, FAD104, IL1RN, ARG2, IL10alpha, INSL3 CRTAP, TNFRSF6, LDLR,VNN1, HLA- 0.82 0.86 0.79 DRA, SOCS3, TGFBI, TNFSF10, IFNGR1, ARG2,FCGR1A GADD45A, VNN1, MKNK1, CCL5, 0.82 0.83 0.82 IL10alpha, PSTPIP2,IRAK2, TNFRSF6, CEACAM1, FAD104, TGFBI HLA-DRA, BCL2A1, PSTPIP2, PFKFB3,0.82 0.82 0.83 JAK2, TNFSF10, ARG2, CEACAM1, IL18R1, MAPK14, CSF1RGADD45B, TNFSF10, TNFSF13B, OSM, 0.82 0.82 0.83 VNN1, PRV1, MKNK1,Gene_MMP9, ANXA3, TGFBI, HLA-DRA GADD45A, IFNGR1, IRAK4, TGFBI, NCR1,0.82 0.75 0.88 FAD104, INSL3, IL10alpha, OSM, TIFA, CSF1R Gene_MMP9,IRAK2, JAK2, TGFBI, 0.82 0.83 0.81 BCL2A1, PSTPIP2, GADD45A, ARG2, OSM,CEACAM1, IFNGR1 MAP2K6, FCGR1A, TNFSF13B, SOD2, 0.82 0.79 0.85 NCR1,ANXA3, TLR4, CD86, ITGAM, IRAK2, INSL3 FAD104, ARG2, NCR1, ANKRD22, OSM,0.82 0.78 0.85 CSF1R, BCL2A1, CRTAP, LY96, SOD2, TNFRSF6 LY96, TDRD9,CD86, GADD45A, ARG2, 0.82 0.82 0.81 VNN1, IL10alpha, SOD2, CRTAP, TIFA,FCGR1A BCL2A1, VNN1, LDLR, TLR4, OSM, 0.82 0.82 0.81 IRAK4, IRAK2,CRTAP, IFNGR1, TGFBI, CD86 CCL5, IFNGR1, TIFA, SOCS3, INSL3, TLR4, 0.820.81 0.82 IRAK4, ANXA3, TGFBI, TDRD9, CSF1R VNN1, SOD2, CCL5, BCL2A1,HLA-DRA, 0.82 0.79 0.85 ANKRD22, CD86, TDRD9, TLR4, FCGR1A, TNFSF10CEACAM1, OSM, IRAK4, MAP2K6, 0.82 0.77 0.85 PSTPIP2, GADD45A, IRAK2,PRV1, IL1RN, TNFSF10, PFKFB3 TNFSF10, IL1RN, IFNGR1, TIFA, FCGR1A, 0.810.82 0.81 PSTPIP2, OSM, ANXA3, TGFBI, INSL3, CRTAP LDLR, VNN1, GADD45B,IL18R1, 0.81 0.8 0.83 GADD45A, Gene_MMP9, FAD104, IL1RN, IRAK4, JAK2,TGFBI FCGR1A, OSM, GADD45A, IL18R1, 0.81 0.79 0.83 GADD45B, TLR4,MAP2K6, CRTAP, TIFA, CCL5, BCL2A1 CSF1R, ITGAM, HLA-DRA, MAP2K6, 0.810.78 0.85 JAK2, FCGR1A, OSM, LDLR, SOCS3, TNFRSF6, IL18R1 IL10alpha,IRAK2, OSM, TIFA, TNFSF10, 0.81 0.75 0.87 FAD104, GADD45B, ITGAM, CD86,VNN1, SOD2 ARG2, GADD45A, LDLR, TNFRSF6, 0.81 0.84 0.78 CEACAM1,ANKRD22, MAPK14, IRAK4, SOD2, INSL3, PSTPIP2 TGFBI, TNFRSF6, IRAK4,IRAK2, OSM, 0.81 0.82 0.79 TNFSF13B, TIFA, FAD104, ANKRD22, MAPK14, CD86VNN1, INSL3, TNFSF10, TGFBI, JAK2, 0.81 0.82 0.8 CRTAP, IRAK2, TNFRSF6,TNFSF13B, LY96, OSM GADD45B, OSM, SOD2, FCGR1A, VNN1, 0.81 0.77 0.85CEACAM1, TIFA, PSTPIP2, IL1RN, TDRD9, LY96 PFKFB3, LDLR, IL10alpha,IRAK4, ANXA3, 0.81 0.77 0.85 NCR1, IL18R1, VNN1, TDRD9, TNFSF13B, CSF1RCD86, TNFRSF6, PFKFB3, MKNK1, OSM, 0.81 0.76 0.85 JAK2, FAD104,IL10alpha, BCL2A1, SOCS3, IRAK4 OSM, GADD45A, TNFSF10, IFNGR1, 0.81 0.750.87 CRTAP, JAK2, ANKRD22, HLA-DRA, TNFSF13B, SOCS3, FCGR1A CCL5, CD86,HLA-DRA, SOCS3, TGFBI, 0.84 0.85 0.82 PSTPIP2, ANXA3, GADD45A, CSF1R,IRAK4, FAD104, MAPK14 IRAK2, CD86, IL1RN, TLR4, ANKRD22, 0.84 0.82 0.85ANXA3, IL10alpha, GADD45B, BCL2A1, CSF1R, INSL3, FCGR1A CD86, TNFRSF6,TIFA, GADD45B, 0.84 0.8 0.87 CEACAM1, TNFSF13B, OSM, IL18R1, CCL5,ITGAM, TGFBI, FAD104 NCR1, CCL5, BCL2A1, IL18R1, ARG2, 0.83 0.82 0.85MKNK1, FCGR1A, CD86, GADD45B, INSL3, IRAK4, ANXA3 TNFSF13B, IFNGR1,Gene_MMP9, SOD2, 0.83 0.8 0.86 LDLR, NCR1, CD86, INSL3, SOCS3, VNN1,PSTPIP2, CEACAM1 SOD2, INSL3, TDRD9, OSM, TNFSF13B, 0.83 0.82 0.84BCL2A1, JAK2, CSF1R, ANXA3, TNFSF10, GADD45A, CRTAP IL10alpha, MKNK1,GADD45A, TGFBI, 0.83 0.79 0.86 MAPK14, IRAK4, TDRD9, IL1RN, TNFRSF6,FCGR1A, ITGAM, CD86 TNFRSF6, IL10alpha, PSTPIP2, HLA-DRA, 0.83 0.78 0.87CRTAP, ARG2, MKNK1, NCR1, OSM, INSL3, VNN1, FAD104 ANXA3, PRV1, LDLR,TNFSF13B, PFKFB3, 0.82 0.8 0.84 TNFRSF6, VNN1, ARG2, ANKRD22, INSL3,NCR1, OSM FCGR1A, HLA-DRA, IFNGR1, CD86, LY96, 0.82 0.76 0.89 ANXA3,MAP2K6, TDRD9, IL18R1, PRV1, SOCS3, TIFA GADD45B, OSM, ITGAM, CSF1R,CD86, 0.82 0.84 0.8 CEACAM1, IFNGR1, SOCS3, MAP2K6, IL1RN, FAD104, CCL5TGFBI, PRV1, JAK2, FCGR1A, ANKRD22, 0.82 0.83 0.81 TNFSF10, VNN1, SOCS3,PSTPIP2, IRAK2, INSL3, FAD104 FCGR1A, GADD45A, SOD2, OSM, ARG2, 0.82 0.80.83 PFKFB3, ANKRD22, IL10alpha, CCL5, SOCS3, CD86, ITGAM LDLR, MAP2K6,INSL3, TDRD9, NCR1, 0.82 0.84 0.79 IL1RN, HLA-DRA, ARG2, MKNK1, MAPK14,OSM, PFKFB3 IL1RN, PFKFB3, TIFA, OSM, IRAK2, 0.82 0.83 0.81 TGFBI,INSL3, TNFSF13B, TNFRSF6, MAP2K6, PSTPIP2, CEACAM1 LY96, TNFSF13B,HLA-DRA, IRAK2, 0.82 0.82 0.82 FCGR1A, ANXA3, CEACAM1, FAD104, TDRD9,IL1RN, ARG2, LDLR IL1RN, ARG2, IRAK2, IRAK4, SOCS3, 0.82 0.81 0.82IL10alpha, CCL5, Gene_MMP9, MAPK14, FAD104, LY96, TGFBI BCL2A1, LY96,ITGAM, OSM, TNFSF10, 0.81 0.81 0.82 INSL3, CD86, IRAK2, MAP2K6, IFNGR1,PRV1, TNFRSF6 BCL2A1, ANXA3, LY96, TNFSF10, NCR1, 0.83 0.83 0.83 OSM,MAPK14, MKNK1, IFNGR1, GADD45A, INSL3, ANKRD22, TNFSF13B LY96, GADD45B,MAPK14, OSM, MKNK1, 0.83 0.82 0.84 BCL2A1, ARG2, IL1RN, INSL3, PFKFB3,LDLR, CRTAP, TIFA OSM, CD86, GADD45B, IRAK4, MAPK14, 0.83 0.82 0.84SOCS3, VNN1, ARG2, TNFSF13B, TDRD9, PRV1, IL1RN, IL18R1 OSM, NCR1,HLA-DRA, TNFSF10, PSTPIP2, 0.83 0.82 0.84 IL1RN, SOCS3, INSL3, TNFRSF6,MAPK14, Gene_MMP9, CEACAM1, IL18R1 CCL5, ARG2, IL10alpha, MAPK14, CSF1R,0.83 0.8 0.85 GADD45B, LDLR, SOD2, Gene_MMP9, IFNGR1, IL18R1, CEACAM1,CD86 TDRD9, SOCS3, Gene_MMP9, IL18R1, 0.83 0.85 0.81 CRTAP, ANXA3, PRV1,ARG2, CD86, ITGAM, OSM, NCR1, VNN1 SOD2, JAK2, PSTPIP2, MAPK14, MAP2K6,0.83 0.83 0.83 FCGR1A, CCL5, ITGAM, CD86, GADD45B, IL1RN, HLA-DRA, VNN1IRAK4, JAK2, SOD2, Gene_MMP9, PSTPIP2, 0.83 0.81 0.84 PFKFB3, HLA-DRA,TNFRSF6, FAD104, ARG2, IFNGR1, IRAK2, MAP2K6 PSTPIP2, MAPK14, CCL5,Gene_MMP9, 0.83 0.79 0.86 TNFRSF6, IL10alpha, LY96, IL1RN, ARG2, SOCS3,TLR4, OSM, HLA-DRA CRTAP, CEACAM1, ARG2, JAK2, 0.83 0.79 0.87 TNFSF10,VNN1, PSTPIP2, IRAK2, TNFRSF6, ITGAM, SOCS3, OSM, IL18R1 VNN1, PSTPIP2,GADD45B, ITGAM, 0.83 0.77 0.88 IL1RN, FAD104, NCR1, TIFA, OSM, TDRD9,SOD2, ARG2, TGFBI TGFBI, IL1RN, INSL3, PSTPIP2, NCR1, 0.82 0.84 0.81FAD104, HLA-DRA, CD86, IRAK4, IL10alpha, ARG2, CSF1R, MAP2K6 FAD104,IRAK2, TIFA, TGFBI, IL18R1, 0.82 0.82 0.83 MAPK14, SOCS3, PSTPIP2, CD86,PRV1, NCR1, FCGR1A, ANXA3 GADD45A, HLA-DRA, INSL3, ANKRD22, 0.82 0.790.85 ANXA3, CD86, IRAK4, GADD45B, PFKFB3, ITGAM, VNN1, NCR1, JAK2 MKNK1,CCL5, PSTPIP2, ANXA3, VNN1, 0.82 0.86 0.78 LY96, IRAK2, IFNGR1, CRTAP,PFKFB3, IL18R1, LDLR, FAD104 TNFSF10, OSM, FCGR1A, IRAK4, TLR4, 0.820.81 0.83 SOCS3, IL18R1, CRTAP, GADD45B, IL1RN, IL10alpha, PRV1, JAK2FAD104, ITGAM, ARG2, PSTPIP2, TLR4, 0.82 0.79 0.84 NCR1, IL1RN, MAP2K6,FCGR1A, PFKFB3, LDLR, IFNGR1, BCL2A1 LDLR, ARG2, NCR1, MKNK1, GADD45B,0.82 0.79 0.84 GADD45A, CEACAM1, PSTPIP2, Gene_MMP9, CCL5, BCL2A1, TIFA,TDRD9 IRAK2, Gene_MMP9, INSL3, ARG2, OSM, 0.81 0.81 0.82 ITGAM, PSTPIP2,TNFSF13B, FCGR1A, BCL2A1, CRTAP, PRV1, MAP2K6 CEACAM1, PSTPIP2, TLR4,IFNGR1, 0.85 0.89 0.83 GADD45B, CSF1R, CD86, VNN1, IL18R1, ANKRD22,MAPK14, OSM, CCL5, IRAK4 LY96, ANKRD22, Gene_MMP9, ARG2, 0.85 0.84 0.86GADD45A, MKNK1, CD86, PSTPIP2, OSM, FAD104, FCGR1A, IL18R1, TIFA, ITGAMARG2, ANKRD22, VNN1, TLR4, OSM, 0.84 0.82 0.86 TIFA, TGFBI, TDRD9,ANXA3, CCL5, TNFRSF6, GADD45B, FAD104, CD86 IFNGR1, TLR4, CRTAP,ANKRD22, 0.84 0.79 0.87 Gene_MMP9, JAK2, INSL3, ITGAM, IRAK4, HLA-DRA,BCL2A1, OSM, TNFSF10, NCR1 TNFSF10, VNN1, TDRD9, CSF1R, OSM, 0.83 0.830.83 IFNGR1, TLR4, PSTPIP2, TIFA, ARG2, FCGR1A, CD86, MAPK14, MAP2K6LDLR, IL18R1, BCL2A1, IL1RN, ARG2, 0.83 0.83 0.84 IRAK2, JAK2, GADD45A,ANKRD22, MAP2K6, OSM, CD86, IRAK4, SOD2 IL1RN, IRAK4, VNN1, CRTAP,TNFSF10, 0.83 0.8 0.86 IFNGR1, FAD104, ARG2, OSM, NCR1, JAK2, ANXA3,CEACAM1, TDRD9 CD86, FCGR1A, MKNK1, TNFRSF6, 0.83 0.78 0.88 GADD45B,LY96, NCR1, PSTPIP2, HLA- DRA, VNN1, ANXA3, IRAK4, ARG2, TGFBI IRAK2,ANKRD22, JAK2, CD86, INSL3, 0.83 0.77 0.88 TNFSF10, OSM, PSTPIP2,IL10alpha, CCL5, TDRD9, GADD45B, Gene_MMP9, LY96 LY96, FCGR1A, CCL5,IL18R1, VNN1, 0.83 0.83 0.83 TNFSF10, MAP2K6, PRV1, IRAK4, IL1RN, TLR4,PSTPIP2, PFKFB3, TGFBI SOD2, IL1RN, JAK2, PRV1, IRAK2, CD86, 0.83 0.830.83 TGFBI, CCL5, MAPK14, TLR4, INSL3, PFKFB3, GADD45B, LY96 TDRD9,FCGR1A, NCR1, IFNGR1, ARG2, 0.83 0.81 0.85 SOD2, TNFRSF6, CD86, PFKFB3,LDLR, JAK2, CCL5, ANKRD22, FAD104 MAPK14, INSL3, MAP2K6, CCL5, CSF1R,0.83 0.82 0.83 CD86, GADD45A, SOCS3, GADD45B, ANXA3, TGFBI, TNFRSF6,IFNGR1, CRTAP GADD45B, MAPK14, GADD45A, IL1RN, 0.83 0.81 0.84 CEACAM1,CRTAP, MKNK1, IL18R1, NCR1, FCGR1A, TIFA, MAP2K6, CD86, TLR4 ARG2,ANKRD22, OSM, LDLR, CCL5, 0.82 0.83 0.82 IL1RN, FCGR1A, PFKFB3, CSF1R,ANXA3, HLA-DRA, INSL3, NCR1, TIFA TNFSF10, ANXA3, OSM, JAK2, VNN1, 0.820.81 0.83 ANKRD22, INSL3, IFNGR1, CD86, MAPK14, GADD45B, TNFRSF6,MAP2K6, LY96 TGFBI, IL18R1, IFNGR1, TDRD9, ANXA3, 0.82 0.8 0.84 TNFSF10,ANKRD22, CD86, TNFRSF6, BCL2A1, FAD104, Gene_MMP9, TNFSF13B, CRTAP OSM,ANXA3, SOCS3, INSL3, ITGAM, 0.82 0.87 0.79 SOD2, NCR1, TNFSF10, BCL2A1,PSTPIP2, TLR4, IRAK2, Gene_MMP9, IL18R1 SOD2, IRAK4, TNFRSF6, PRV1,FCGR1A, 0.82 0.84 0.8 LDLR, MAP2K6, TIFA, CEACAM1, IL18R1, SOCS3, OSM,IL10alpha, MKNK1 TLR4, MKNK1, SOD2, SOCS3, FAD104, 0.85 0.83 0.86HLA-DRA, PSTPIP2, ANKRD22, TIFA, TNFRSF6, JAK2, TNFSF10, ARG2, CSF1R,TLR4 CCL5, MAP2K6, SOCS3, IFNGR1, TGFBI, 0.84 0.85 0.83 ANXA3, OSM,GADD45A, TNFSF10, Gene_MMP9, TNFRSF6, TIFA, ARG2, INSL3, SOCS3 ANXA3,IL18R1, VNN1, NCR1, TIFA, 0.84 0.84 0.83 INSL3, TGFBI, MAPK14, CEACAM1,CRTAP, CSF1R, TDRD9, IL10alpha, CCL5, MAPK14 TLR4, MKNK1, SOD2, SOCS3,FAD104, 0.85 0.83 0.86 HLA-DRA, PSTPIP2, ANKRD22, TIFA, TNFRSF6, JAK2,TNFSF10, ARG2, CSF1R, IRAK4 CCL5, MAP2K6, SOCS3, IFNGR1, TGFBI, 0.840.85 0.83 ANXA3, OSM, GADD45A, TNFSF10, Gene_MMP9, TNFRSF6, TIFA, ARG2,INSL3, TLR4 ANXA3, IL18R1, VNN1, NCR1, TIFA, 0.84 0.84 0.83 INSL3,TGFBI, MAPK14, CEACAM1, CRTAP, CSF1R, TDRD9, IL10alpha, CCL5, SOCS3IL18R1, MAP2K6, INSL3, IRAK4, CCL5, 0.84 0.79 0.88 PFKFB3, CSF1R, LDLR,ITGAM, GADD45A, ARG2, PSTPIP2, TLR4, CD86, MAPK14 SOD2, IFNGR1, CEACAM1,OSM, FAD104, 0.83 0.84 0.83 HLA-DRA, CRTAP, IL10alpha, TGFBI, GADD45A,ITGAM, IL18R1, CCL5, TLR4, FCGR1A SOCS3, OSM, TIFA, TNFRSF6, INSL3, 0.830.83 0.84 LDLR, IL18R1, PFKFB3, TGFBI, IL10alpha, GADD45B, ARG2,TNFSF10, VNN1, ANXA3 PRV1, PFKFB3, CEACAM1, FCGR1A, TIFA, 0.83 0.82 0.85MKNK1, ARG2, GADD45B, IL18R1, CD86, ITGAM, VNN1, IFNGR1, OSM, JAK2 NCR1,INSL3, HLA-DRA, TNFSF10, 0.83 0.82 0.84 TNFRSF6, FCGR1A, OSM, GADD45B,MKNK1, TNFSF13B, CSF1R, LY96, MAPK14, PRV1, CCL5 FCGR1A, CD86, CEACAM1,ANXA3, 0.83 0.79 0.87 FAD104, CRTAP, JAK2, MKNK1, MAPK14, IFNGR1,GADD45A, PFKFB3, ANKRD22, IL18R1, LY96 IRAK2, IL10alpha, INSL3, FAD104,TIFA, 0.83 0.78 0.87 SOD2, IFNGR1, IL1RN, HLA-DRA, LY96, IL18R1, CCL5,CD86, TDRD9, TNFSF10 LY96, BCL2A1, Gene_MMP9, OSM, ARG2, 0.83 0.8 0.85MAP2K6, INSL3, ITGAM, MAPK14, TIFA, IRAK2, PSTPIP2, FCGR1A, CEACAM1,IFNGR1 IL18R1, BCL2A1, PFKFB3, Gene_MMP9, 0.83 0.76 0.88 IL1RN,IL10alpha, SOCS3, PSTPIP2, CRTAP, OSM, CD86, FCGR1A, FAD104, JAK2, SOD2MKNK1, CRTAP, PRV1, IL1RN, GADD45A, 0.82 0.82 0.82 TNFRSF6, FAD104,HLA-DRA, CEACAM1, PSTPIP2, OSM, JAK2, IL18R1, LDLR, IRAK4 FCGR1A,BCL2A1, IFNGR1, CRTAP, VNN1, 0.82 0.8 0.84 TIFA, CCL5, NCR1, OSM,HLA-DRA, IRAK4, INSL3, MAP2K6, TNFSF13B, ARG2 FAD104, BCL2A1, PRV1,MKNK1, CRTAP, 0.82 0.77 0.87 IRAK4, PFKFB3, SOD2, CD86, ARG2, FCGR1A,ANKRD22, INSL3, IFNGR1, LDLR SOCS3, CD86, FCGR1A, MAP2K6, TGFBI, 0.820.84 0.8 IRAK2, PSTPIP2, CCL5, IL1RN, GADD45B, TDRD9, OSM, IL10alpha,PFKFB3, FAD104 TIFA, SOD2, LDLR, FCGR1A, BCL2A1, 0.86 0.84 0.87TNFSF13B, ARG2, PSTPIP2, MAPK14, LY96, Gene_MMP9, IFNGR1, GADD45B,ANXA3, PRV1, CD86 HLA-DRA, IRAK2, FCGR1A, ANXA3, 0.84 0.83 0.84 ITGAM,LY96, TDRD9, SOCS3, IL1RN, PFKFB3, GADD45B, TNFSF13B, TLR4, ARG2, CSF1R,FAD104 OSM, CRTAP, CEACAM1, NCR1, IRAK4, 0.83 0.81 0.86 TLR4, FAD104,MKNK1, TDRD9, PSTPIP2, IL1RN, CSF1R, MAP2K6, ITGAM, ARG2, IFNGR1 TIFA,IL10alpha, VNN1, OSM, MAP2K6, 0.83 0.77 0.9 GADD45B, PSTPIP2, TDRD9,TNFRSF6, INSL3, IL1RN, FAD104, TNFSF10, TGFBI, IL18R1, TLR4 GADD45A,CSF1R, INSL3, BCL2A1, 0.83 0.82 0.84 TDRD9, LDLR, HLA-DRA, MAP2K6,PSTPIP2, CCL5, ANXA3, PRV1, TNFRSF6, TLR4, CD86, JAK2 TDRD9, PFKFB3,MAPK14, IL1RN, 0.83 0.85 0.8 IFNGR1, FCGR1A, MAP2K6, TNFRSF6, ARG2,VNN1, CRTAP, LDLR, CEACAM1, FAD104, NCR1, TNFSF10 ARG2, IL10alpha, TLR4,PRV1, INSL3, 0.83 0.82 0.83 OSM, CD86, TGFBI, SOCS3, GADD45B, TIFA,LDLR, IRAK2, GADD45A, SOD2, TNFSF13B TNFSF10, PRV1, SOCS3, FAD104, 0.830.8 0.85 TNFRSF6, ARG2, Gene_MMP9, FCGR1A, TGFBI, NCR1, CRTAP, MAP2K6,ANXA3, CSF1R, HLA-DRA, JAK2 TNFRSF6, BCL2A1, VNN1, ANXA3, SOCS3, 0.830.8 0.85 GADD45A, CRTAP, CCL5, FAD104, ANKRD22, MKNK1, FCGR1A, SOD2,IRAK2, MAPK14, Gene_MMP9 FAD104, OSM, LDLR, TNFSF10, GADD45B, 0.82 0.780.87 HLA-DRA, TNFRSF6, GADD45A, CD86, TDRD9, ITGAM, ANXA3, IFNGR1,MAPK14, CSF1R, TGFBI CSF1R, PRV1, ANXA3, SOD2, PSTPIP2, 0.82 0.77 0.87CEACAM1, IFNGR1, IRAK4, LY96, MAPK14, IL10alpha, MKNK1, TNFRSF6, OSM,TGFBI, INSL3 PSTPIP2, ARG2, MAP2K6, INSL3, SOCS3, 0.82 0.76 0.89 JAK2,FAD104, ANKRD22, HLA-DRA, ITGAM, GADD45B, LY96, IRAK2, PFKFB3, TNFRSF6,IFNGR1 CRTAP, MKNK1, BCL2A1, PRV1, CD86, 0.82 0.88 0.77 TNFRSF6,PSTPIP2, MAPK14, TNFSF13B, ARG2, PFKFB3, CEACAM1, FAD104, Gene_MMP9,OSM, SOD2 MKNK1, SOCS3, CRTAP, FCGR1A, CD86, 0.82 0.82 0.82 IL10alpha,GADD45A, IL18R1, IRAK2, CCL5, JAK2, ANKRD22, TIFA, TGFBI, CSF1R, BCL2A1GADD45B, CEACAM1, ANKRD22, IRAK4, 0.82 0.81 0.82 LDLR, CRTAP, MKNK1,OSM, MAPK14, MAP2K6, INSL3, GADD45A, PFKFB3, TNFSF10, CSF1R, TIFA CSF1R,INSL3, TNFRSF6, BCL2A1, CD86, 0.82 0.79 0.85 CEACAM1, IL10alpha, IL18R1,TLR4, ITGAM, TNFSF10, OSM, ARG2, SOD2, FCGR1A, PSTPIP2 NCR1, LDLR,MKNK1, INSL3, BCL2A1, 0.82 0.78 0.85 JAK2, FCGR1A, IL1RN, TNFRSF6, PRV1,GADD45B, ARG2, MAP2K6, OSM, VNN1, TDRD9 TLR4, CD86, MAPK14, TNFSF13B,INSL3, 0.82 0.85 0.79 CRTAP, NCR1, ARG2, GADD45A, CSF1R, TNFRSF6,MAP2K6, JAK2, MKNK1, ANKRD22, OSM CSF1R, CCL5, ARG2, BCL2A1, FCGR1A,0.82 0.85 0.79 MKNK1, TDRD9, IFNGR1, PFKFB3, ITGAM, JAK2, OSM, GADD45B,FAD104, NCR1, HLA-DRA IFNGR1, FCGR1A, TLR4, OSM, PSTPIP2, 0.86 0.82 0.9IL18R1, NCR1, SOCS3, PFKFB3, INSL3, LDLR, TNFRSF6, SOD2, GADD45B,IL10alpha, CCL5, IL1RN CEACAM1, IL18R1, SOCS3, CRTAP, LDLR, 0.86 0.840.88 HLA-DRA, LY96, IL1RN, IL10alpha, BCL2A1, GADD45A, TIFA, FAD104,ANKRD22, OSM, CCL5, IFNGR1 FAD104, GADD45B, HLA-DRA, VNN1, 0.85 0.790.91 IL10alpha, CD86, JAK2, INSL3, TDRD9, TLR4, IRAK4, SOD2, LDLR, CCL5,MKNK1, ARG2, IL18R1 IL18R1, PRV1, IL1RN, TNFSF10, FAD104, 0.85 0.81 0.89ITGAM, FCGR1A, INSL3, MAP2K6, LDLR, TNFSF13B, IRAK2, OSM, PFKFB3, TGFBI,IL10alpha, LY96 IRAK2, HLA-DRA, IFNGR1, MAP2K6, 0.85 0.81 0.89 TLR4,ITGAM, SOCS3, CD86, ARG2, VNN1, IL18R1, ANXA3, FCGR1A, IL1RN, Gene_MMP9,TGFBI, IL10alpha MAP2K6, IL18R1, IL1RN, CSF1R, 0.84 0.81 0.87 TNFRSF6,FCGR1A, NCR1, TDRD9, TNFSF10, SOCS3, CCL5, IFNGR1, TIFA, CRTAP, GADD45B,IL10alpha, TGFBI CD86, CCL5, IRAK4, GADD45A, ANXA3, 0.84 0.8 0.88 OSM,JAK2, INSL3, SOCS3, BCL2A1, FAD104, MAPK14, TIFA, TLR4, NCR1, PRV1,TDRD9 BCL2A1, IL18R1, TLR4, OSM, CD86, 0.84 0.8 0.89 FAD104, PRV1, JAK2,MAPK14, TNFRSF6, CEACAM1, IL1RN, IL10alpha, SOD2, Gene_MMP9, CSF1R,PFKFB3 LY96, TIFA, IL10alpha, ANXA3, LDLR, 0.84 0.81 0.87 JAK2, IFNGR1,IRAK2, MAP2K6, TGFBI, MAPK14, TDRD9, FCGR1A, ITGAM, TNFSF10, GADD45B,SOCS3 TNFSF13B, FAD104, SOD2, SOCS3, 0.84 0.85 0.83 CEACAM1, TDRD9,ARG2, CD86, IRAK2, PFKFB3, FCGR1A, NCR1, MAPK14, CRTAP, LDLR, GADD45A,TNFRSF6 IRAK2, MKNK1, PSTPIP2, ANXA3, HLA- 0.84 0.83 0.84 DRA, TNFSF10,IFNGR1, PFKFB3, OSM, PRV1, IL1RN, IL10alpha, FAD104, CD86, TIFA, BCL2A1,TNFSF13B VNN1, IFNGR1, LY96, SOD2, IL18R1, 0.84 0.82 0.86 SOCS3, FCGR1A,ARG2, CSF1R, Gene_MMP9, IRAK4, MAP2K6, TIFA, FAD104, HLA-DRA, GADD45B,IL1RN CD86, Gene_MMP9, IL18R1, TNFSF13B, 0.83 0.82 0.84 FCGR1A, TNFRSF6,INSL3, IL1RN, PFKFB3, PSTPIP2, NCR1, GADD45B, VNN1, CRTAP, IRAK4,MAP2K6, OSM TNFSF13B, FAD104, PRV1, TIFA, SOD2, 0.83 0.79 0.88 TDRD9,TLR4, TNFRSF6, MKNK1, OSM, MAP2K6, CCL5, ARG2, LDLR, HLA-DRA, PSTPIP2,IL18R1 IRAK4, MAP2K6, JAK2, LY96, ITGAM, 0.83 0.82 0.84 CCL5, CSF1R,ARG2, FCGR1A, FAD104, CD86, TNFSF10, IL18R1, CRTAP, GADD45A, TLR4,Gene_MMP9 IRAK2, OSM, MAP2K6, TNFSF13B, 0.83 0.8 0.86 ANKRD22, HLA-DRA,SOD2, TNFSF10, VNN1, ARG2, IRAK4, LY96, IFNGR1, JAK2, BCL2A1, FCGR1A,CSF1R IL10alpha, FCGR1A, TGFBI, ANKRD22, 0.83 0.78 0.87 IRAK4, CD86,TNFSF13B, TNFRSF6, IL18R1, JAK2, IL1RN, PSTPIP2, OSM, MAP2K6, GADD45A,Gene_MMP9, MAPK14 PRV1, IRAK4, MKNK1, JAK2, OSM, 0.82 0.83 0.82 MAP2K6,BCL2A1, GADD45B, Gene_MMP9, IL10alpha, FAD104, ARG2, PSTPIP2, SOD2,TNFRSF6, TNFSF10, IL1RN IL1RN, OSM, FAD104, CRTAP, IRAK4, 0.82 0.82 0.83IL10alpha, LDLR, INSL3, TNFSF10, CCL5, IL18R1, ANXA3, PRV1, ARG2,Gene_MMP9, CEACAM1, SOCS3 TNFRSF6, MAP2K6, FCGR1A, MAPK14, 0.82 0.810.83 ARG2, INSL3, TNFSF10, NCR1, PRV1, CEACAM1, ANXA3, IL18R1, TIFA,IFNGR1, IRAK4, CCL5, VNN1 IRAK2, ANKRD22, MAPK14, TIFA, 0.85 0.83 0.87GADD45B, OSM, IL10alpha, SOD2, CCL5, GADD45A, CD86, IRAK4, SOCS3, TDRD9,MAP2K6, FAD104, PRV1, ANXA3 TLR4, LDLR, OSM, MAP2K6, GADD45A, 0.85 0.840.86 TIFA, NCR1, IL18R1, IFNGR1, INSL3, ANXA3, IL10alpha, IL1RN, CSF1R,GADD45B, PFKFB3, TGFBI, CRTAP HLA-DRA, GADD45A, ANXA3, ARG2, 0.84 0.850.83 FAD104, PFKFB3, ITGAM, JAK2, MAPK14, OSM, CD86, LDLR, TIFA, CCL5,NCR1, IRAK2, SOD2, PRV1 GADD45A, INSL3, IRAK2, TNFSF10, 0.83 0.82 0.85TGFBI, IRAK4, NCR1, HLA-DRA, CEACAM1, GADD45B, MAPK14, CD86, IL18R1,CRTAP, ANKRD22, PSTPIP2, LY96, PFKFB3 MAP2K6, IL1RN, TIFA, TLR4, OSM,TGFBI, 0.83 0.85 0.81 ANXA3, NCR1, IL18R1, ANKRD22, GADD45A, TNFSF10,PRV1, IRAK2, TDRD9, JAK2, Gene_MMP9, CSF1R CD86, GADD45A, GADD45B,TNFSF13B, 0.83 0.83 0.83 CRTAP, TNFRSF6, NCR1, IL10alpha, CSF1R, OSM,MKNK1, CEACAM1, TLR4, IFNGR1, IRAK2, SOCS3, TGFBI, Gene_MMP9 BCL2A1,ANKRD22, OSM, CD86, ITGAM, 0.83 0.82 0.84 ANXA3, FCGR1A, CCL5, TIFA,IRAK4, HLA-DRA, NCR1, CRTAP, TLR4, CEACAM1, FAD104, ARG2, MAP2K6GADD45A, IFNGR1, MAP2K6, CRTAP, 0.83 0.82 0.84 MAPK14, TNFSF10, LDLR,TIFA, OSM, SOCS3, CD86, ARG2, PSTPIP2, IL1RN, LY96, GADD45B, ANKRD22,TGFBI INSL3, TLR4, BCL2A1, ANKRD22, FAD104, 0.83 0.81 0.85 MAP2K6,GADD45B, ARG2, NCR1, MKNK1, ITGAM, CSF1R, IL1RN, HLA- DRA, LDLR, CRTAP,PRV1, LY96 CRTAP, HLA-DRA, ARG2, PSTPIP2, 0.83 0.81 0.86 MKNK1, INSL3,TIFA, CEACAM1, JAK2, Gene_MMP9, TLR4, IRAK4, CD86, FAD104, CCL5,TNFSF10, LDLR, IFNGR1 IL18R1, TNFRSF6, PFKFB3, FAD104, 0.83 0.8 0.86GADD45A, OSM, JAK2, VNN1, MKNK1, BCL2A1, SOCS3, NCR1, TLR4, FCGR1A,CSF1R, ITGAM, IRAK4, CRTAP FAD104, TNFRSF6, OSM, TIFA, PSTPIP2, 0.830.78 0.87 ANXA3, TLR4, CD86, IRAK4, TNFSF13B, IL1RN, IFNGR1, ITGAM,BCL2A1, CEACAM1, MKNK1, TGFBI, ARG2 TNFSF10, BCL2A1, TGFBI, LY96, PRV1,0.83 0.85 0.81 MKNK1, SOD2, ARG2, SOCS3, CD86, IL10alpha, TNFSF13B,ITGAM, OSM, MAPK14, PSTPIP2, ANXA3, CCL5 SOCS3, OSM, CCL5, JAK2, MAP2K6,0.83 0.85 0.81 IL18R1, NCR1, CEACAM1, IRAK2, ARG2, LY96, PRV1, ITGAM,TNFSF13B, TNFSF10, TGFBI, IL10alpha, LDLR ARG2, IRAK2, Gene_MMP9,GADD45B, 0.83 0.83 0.82 MKNK1, PFKFB3, MAPK14, IRAK4, CSF1R, FCGR1A,GADD45A, TDRD9, TIFA, CD86, IL18R1, BCL2A1, CRTAP, TNFRSF6 FAD104,IL1RN, TGFBI, TLR4, BCL2A1, 0.83 0.82 0.83 IFNGR1, IRAK4, PRV1, ANKRD22,CRTAP, TNFRSF6, CSF1R, ARG2, OSM, GADD45A, VNN1, INSL3, CEACAM1 CSF1R,SOCS3, FAD104, TLR4, INSL3, 0.83 0.81 0.84 ANXA3, NCR1, CRTAP, IFNGR1,TIFA, OSM, MAPK14, TDRD9, IL1RN, ANKRD22, TNFRSF6, IRAK2, BCL2A1 MAP2K6,IFNGR1, CD86, FCGR1A, IRAK2, 0.83 0.81 0.84 MKNK1, CRTAP, FAD104,IL10alpha, VNN1, ANXA3, NCR1, IL18R1, CEACAM1, CCL5, ARG2, MAPK14, SOCS3IL1RN, IRAK4, JAK2, CD86, BCL2A1, 0.83 0.79 0.86 TGFBI, Gene_MMP9, NCR1,IFNGR1, VNN1, SOCS3, CCL5, TNFSF13B, TDRD9, MAPK14, PRV1, OSM, TLR4MAP2K6, CSF1R, HLA-DRA, ANKRD22, 0.82 0.8 0.85 MKNK1, SOCS3, TNFSF10,LDLR, FAD104, CEACAM1, TNFSF13B, TDRD9, IRAK4, VNN1, IL18R1, OSM,PSTPIP2, Gene_MMP9 CCL5, SOD2, JAK2, IRAK4, IRAK2, 0.84 0.79 0.89Gene_MMP9, IFNGR1, TLR4, GADD45A, TNFSF10, CSF1R, IL18R1, PRV1,TNFSF13B, HLA-DRA, LDLR, CD86, SOCS3, FAD104 MAP2K6, TNFSF13B, SOD2,GADD45B, 0.84 0.84 0.84 HLA-DRA, CSF1R, CCL5, TIFA, NCR1, IFNGR1, OSM,CD86, SOCS3, ARG2, IL10alpha, BCL2A1, TDRD9, LDLR, GADD45A IFNGR1, OSM,MAPK14, CEACAM1, 0.84 0.83 0.85 PFKFB3, TLR4, CSF1R, JAK2, IL18R1,TGFBI, CD86, IL10alpha, INSL3, BCL2A1, FCGR1A, GADD45B, LDLR, PSTPIP2,FAD104 ARG2, PRV1, IRAK4, TNFRSF6, MAP2K6, 0.84 0.8 0.86 SOCS3, IL18R1,HLA-DRA, IFNGR1, ANXA3, TNFSF10, JAK2, FCGR1A, GADD45A, INSL3, IL1RN,TNFSF13B, ITGAM, CSF1R LDLR, INSL3, JAK2, TNFRSF6, PRV1, 0.83 0.82 0.84IFNGR1, OSM, ITGAM, FCGR1A, IL10alpha, NCR1, TDRD9, MAP2K6, TNFSF13B,TIFA, HLA-DRA, ANKRD22, GADD45B, IL1RN MAPK14, SOD2, CSF1R, ITGAM,MAP2K6, 0.83 0.82 0.84 TLR4, ANXA3, BCL2A1, CRTAP, IL10alpha, IRAK4,CCL5, SOCS3, TNFSF13B, ARG2, FCGR1A, CEACAM1, OSM, IL1RN LY96,IL10alpha, GADD45A, GADD45B, 0.83 0.78 0.87 IL1RN, IL18R1, PSTPIP2,ARG2, IRAK2, CEACAM1, MKNK1, PFKFB3, TNFSF10, ANKRD22, ANXA3, SOD2,MAP2K6, IRAK4, SOCS3 IL18R1, MAP2K6, ARG2, CD86, TNFSF13B, 0.82 0.820.83 MAPK14, TNFSF10, CRTAP, GADD45A, NCR1, GADD45B, JAK2, MKNK1,TNFRSF6, VNN1, FAD104, LY96, CEACAM1, PRV1 HLA-DRA, CD86, SOCS3, TIFA,TNFSF13B, 0.82 0.79 0.85 FCGR1A, JAK2, PFKFB3, MAP2K6, OSM, TGFBI,ANKRD22, CEACAM1, IRAK4, ARG2, IL18R1, SOD2, MKNK1, GADD45B TDRD9,IRAK2, PFKFB3, CSF1R, TGFBI, 0.82 0.79 0.85 SOCS3, IL10alpha, IFNGR1,TNFRSF6, VNN1, FCGR1A, PRV1, TNFSF13B, MAPK14, BCL2A1, CD86, SOD2,INSL3, ARG2 LDLR, ITGAM, IL18R1, ANXA3, 0.82 0.79 0.86 GADD45A, VNN1,TDRD9, LY96, BCL2A1, CD86, IRAK2, FAD104, Gene_MMP9, TLR4, TIFA, OSM,ARG2, CRTAP, PSTPIP2 CCL5, TGFBI, BCL2A1, VNN1, TDRD9, 0.82 0.8 0.84SOCS3, CRTAP, CD86, TNFRSF6, LDLR, CSF1R, PRV1, IL18R1, INSL3, GADD45B,TNFSF13B, PFKFB3, JAK2, SOD2 SOD2, ARG2, HLA-DRA, LY96, 0.82 0.8 0.84Gene_MMP9, VNN1, CD86, IL10alpha, CSF1R, PSTPIP2, JAK2, TNFSF13B, IRAK2,CCL5, ANKRD22, TLR4, IL1RN, OSM, GADD45B SOCS3, TGFBI, FCGR1A, TDRD9,0.82 0.78 0.86 GADD45A, TIFA, IFNGR1, VNN1, ITGAM, MAPK14, OSM, ANXA3,TNFSF13B, IL1RN, HLA-DRA, ARG2, MAP2K6, TLR4, PSTPIP2 CD86, INSL3,MAPK14, TIFA, MAP2K6, 0.82 0.76 0.87 Gene_MMP9, CRTAP, CSF1R, MKNK1,IL10alpha, FAD104, PRV1, BCL2A1, NCR1, LDLR, IRAK4, HLA-DRA, IFNGR1,TDRD9 NCR1, LDLR, IRAK2, TNFRSF6, CD86, 0.82 0.74 0.89 SOD2, TNFSF13B,VNN1, GADD45A, Gene_MMP9, PFKFB3, ANKRD22, PSTPIP2, PRV1, FCGR1A,IL18R1, TIFA, INSL3, CRTAP IL1RN, TLR4, PSTPIP2, IL18R1, GADD45A, 0.820.82 0.82 IL10alpha, BCL2A1, MKNK1, IRAK2, HLA- DRA, ANKRD22, NCR1,CEACAM1, IRAK4, OSM, TIFA, SOD2, TGFBI, Gene_MMP9 GADD45A, LY96, ITGAM,CCL5, TNFSF10, 0.82 0.78 0.85 TNFSF13B, HLA-DRA, CSF1R, TIFA, SOCS3,MKNK1, ARG2, IFNGR1, IL1RN, BCL2A1, OSM, PFKFB3, PSTPIP2, IRAK2Gene_MMP9, GADD45A, PSTPIP2, INSL3, 0.82 0.78 0.85 IRAK4, HLA-DRA, CCL5,TGFBI, OSM, LY96, TDRD9, NCR1, PFKFB3, IFNGR1, IRAK2, VNN1, CRTAP, TIFA,CD86 LDLR, ARG2, MAP2K6, MAPK14, IL18R1, 0.85 0.81 0.9 CCL5, PSTPIP2,ANKRD22, OSM, TDRD9, HLA-DRA, SOCS3, ANXA3, TNFRSF6, TIFA, CD86, FAD104,MKNK1, BCL2A1, IRAK2 FCGR1A, FAD104, Gene_MMP9, LDLR, 0.85 0.8 0.88ANKRD22, VNN1, SOCS3, TNFSF13B, TLR4, TDRD9, CEACAM1, PSTPIP2, MAPK14,ARG2, IRAK4, OSM, PRV1, TNFRSF6, IL10alpha, PFKFB3 TNFSF10, IRAK2,TDRD9, TGFBI, PFKFB3, 0.84 0.8 0.88 CD86, OSM, IFNGR1, FAD104, ANXA3,CCL5, IRAK4, PSTPIP2, GADD45A, SOCS3, CSF1R, NCR1, CRTAP, IL1RN, BCL2A1IFNGR1, TIFA, ARG2, IRAK2, CCL5, LDLR, 0.84 0.83 0.85 OSM, SOCS3, SOD2,IL1RN, PSTPIP2, BCL2A1, FAD104, IL18R1, IL10alpha, CD86, FCGR1A, ITGAM,JAK2, Gene_MMP9 PSTPIP2, SOCS3, OSM, FCGR1A, IL1RN, 0.84 0.82 0.85IRAK4, ITGAM, ARG2, TGFBI, Gene_MMP9, CSF1R, TLR4, GADD45A, GADD45B,PRV1, IFNGR1, IL18R1, VNN1, FAD104, PFKFB3 TNFRSF6, TIFA, PFKFB3, PRV1,OSM, 0.84 0.8 0.87 JAK2, TGFBI, IL10alpha, CEACAM1, INSL3, IRAK2, LY96,ARG2, CD86, FAD104, MAP2K6, TLR4, SOCS3, IL18R1, ITGAM FCGR1A, HLA-DRA,ARG2, CRTAP, 0.84 0.83 0.84 CEACAM1, TNFSF13B, OSM, ANXA3, IL1RN,Gene_MMP9, TNFRSF6, FAD104, JAK2, IFNGR1, MKNK1, LDLR, IL10alpha, TGFBI,SOD2, CCL5 GADD45A, MAPK14, ARG2, TDRD9, NCR1, 0.84 0.83 0.85 IL18R1,SOD2, ITGAM, FCGR1A, SOCS3, HLA-DRA, IRAK4, TNFRSF6, PRV1, CD86, TGFBI,TNFSF13B, TIFA, VNN1, FAD104 HLA-DRA, ARG2, IL1RN, SOCS3, PSTPIP2, 0.840.79 0.88 CCL5, IFNGR1, CD86, TLR4, TGFBI, LY96, TNFRSF6, OSM, MAP2K6,VNN1, ITGAM, TNFSF10, NCR1, IRAK4, MAPK14 BCL2A1, ITGAM, ANKRD22, ARG2,0.84 0.79 0.87 FAD104, OSM, GADD45A, CCL5, TGFBI, CD86, PSTPIP2, PFKFB3,IFNGR1, IL18R1, CEACAM1, Gene_MMP9, IRAK2, IL1RN, NCR1, LY96 JAK2, VNN1,CSF1R, TLR4, OSM, SOCS3, 0.84 0.79 0.88 ANXA3, LY96, MKNK1, TDRD9,ITGAM, Gene_MMP9, TGFBI, CEACAM1, CD86, MAP2K6, CCL5, TNFSF10, IL1RN,IL18R1 FAD104, PSTPIP2, CEACAM1, MAP2K6, 0.83 0.84 0.83 TIFA, ANKRD22,INSL3, TLR4, CRTAP, LY96, SOCS3, MAPK14, JAK2, ARG2, MKNK1, IL18R1,CSF1R, CD86, PRV1, OSM CEACAM1, SOCS3, FCGR1A, ARG2, 0.83 0.8 0.87INSL3, FAD104, IRAK4, GADD45A, ITGAM, PRV1, TNFSF13B, NCR1, Gene_MMP9,IL18R1, SOD2, MAPK14, TIFA, IRAK2, ANKRD22, IL1RN GADD45B, SOD2, CRTAP,OSM, 0.83 0.78 0.88 TNFSF13B, CCL5, CD86, INSL3, HLA-DRA, TNFRSF6,TGFBI, GADD45A, FCGR1A, FAD104, JAK2, IL1RN, PFKFB3, MAP2K6, CEACAM1,TDRD9 FCGR1A, GADD45A, ANKRD22, IL1RN, 0.83 0.78 0.88 PFKFB3, CCL5,TIFA, IL10alpha, CRTAP, MKNK1, PSTPIP2, PRV1, CSF1R, ANXA3, NCR1, JAK2,VNN1, IRAK4, CD86, MAP2K6 TLR4, GADD45A, JAK2, OSM, CD86, 0.83 0.78 0.88SOCS3, CEACAM1, IL18R1, MAP2K6, PRV1, FAD104, BCL2A1, VNN1, INSL3,PSTPIP2, ANKRD22, TNFSF10, IFNGR1, CRTAP, HLA-DRA FAD104, IL18R1, TIFA,TNFRSF6, 0.83 0.77 0.88 Gene_MMP9, ARG2, OSM, TNFSF13B, FCGR1A, CD86,CEACAM1, LY96, NCR1, TNFSF10, PFKFB3, PRV1, GADD45A, SOCS3, HLA-DRA,IRAK2 TDRD9, MKNK1, PFKFB3, IRAK2, INSL3, 0.83 0.82 0.84 ITGAM, MAPK14,JAK2, HLA-DRA, CSF1R, CRTAP, NCR1, SOD2, TIFA, IRAK4, CD86, OSM, BCL2A1,LY96, ANKRD22 ANKRD22, CRTAP, NCR1, OSM, INSL3, 0.83 0.82 0.84 CD86,CCL5, JAK2, CSF1R, GADD45B, ANXA3, SOCS3, PSTPIP2, FCGR1A, HLA- DRA,IRAK2, IL1RN, IL18R1, PFKFB3, Gene_MMP9 IL1RN, LY96, ARG2, PRV1,GADD45A, 0.85 0.84 0.85 TNFSF10, FCGR1A, IL10alpha, LDLR, PFKFB3, CRTAP,SOD2, CEACAM1, IL18R1, CCL5, PSTPIP2, TLR4, VNN1, HLA-DRA, JAK2, ANKRD22CD86, LDLR, CRTAP, OSM, TGFBI, 0.84 0.88 0.82 FCGR1A, NCR1, MAPK14,GADD45A, ARG2, TLR4, GADD45B, INSL3, TNFSF10, ANXA3, MKNK1, PSTPIP2,CSF1R, SOD2, MAP2K6, BCL2A1 IRAK4, GADD45A, MAP2K6, ANKRD22, 0.84 0.820.85 Gene_MMP9, TDRD9, PSTPIP2, VNN1, IL18R1, ARG2, IL1RN, PFKFB3,FCGR1A, TNFRSF6, JAK2, NCR1, TLR4, FAD104, SOCS3, IFNGR1, SOD2 SOCS3,ITGAM, Gene_MMP9, MKNK1, 0.84 0.82 0.86 ARG2, CRTAP, BCL2A1, PRV1, NCR1,HLA-DRA, MAP2K6, FCGR1A, CD86, FAD104, CCL5, TGFBI, TDRD9, OSM, GADD45B,IRAK4, LY96 INSL3, BCL2A1, PSTPIP2, OSM, MAP2K6, 0.83 0.84 0.83 CCL5,MKNK1, FAD104, ITGAM, MAPK14, IL1RN, VNN1, IRAK2, FCGR1A, CD86, PFKFB3,TDRD9, HLA-DRA, ARG2, TLR4, CEACAM1 TIFA, MKNK1, TNFSF13B, CSF1R, HLA-0.83 0.82 0.84 DRA, IL18R1, MAPK14, INSL3, PFKFB3, ANKRD22, LDLR, ARG2,CCL5, LY96, PSTPIP2, GADD45A, CEACAM1, JAK2, TGFBI, VNN1, IL1RN CRTAP,FAD104, TIFA, BCL2A1, IRAK2, 0.83 0.81 0.85 PSTPIP2, PFKFB3, MKNK1,ANKRD22, IL18R1, GADD45B, TDRD9, TLR4, INSL3, CEACAM1, MAP2K6, ARG2,CD86, NCR1, TNFSF13B, PRV1 JAK2, SOCS3, IFNGR1, IL1RN, OSM, 0.83 0.810.85 BCL2A1, SOD2, ITGAM, FAD104, IL18R1, PSTPIP2, ARG2, PRV1, TNFSF13B,FCGR1A, IRAK2, IL10alpha, PFKFB3, MAPK14, INSL3, TGFBI GADD45A, CCL5,LDLR, ARG2, IRAK2, 0.83 0.86 0.8 SOCS3, SOD2, PRV1, MAP2K6, INSL3,TNFSF10, IL18R1, IL1RN, MAPK14, FAD104, IFNGR1, HLA-DRA, PSTPIP2, ITGAM,CSF1R, IL10alpha CD86, TGFBI, ITGAM, IL10alpha, JAK2, 0.83 0.79 0.86TIFA, FAD104, CRTAP, IL1RN, BCL2A1, CCL5, GADD45B, HLA-DRA, SOD2, OSM,NCR1, VNN1, IL18R1, ANXA3, Gene_MMP9, PSTPIP2 IL10alpha, TNFSF13B,GADD45B, MAP2K6, 0.83 0.76 0.89 CCL5, IRAK2, MKNK1, LDLR, VNN1, GADD45A,ARG2, OSM, IFNGR1, IL18R1, ANKRD22, JAK2, TLR4, TGFBI, TNFRSF6, FAD104,PFKFB3 MAPK14, SOD2, PRV1, GADD45B, 0.83 0.83 0.82 MKNK1, IL18R1, INSL3,NCR1, LY96, IRAK2, CSF1R, TNFRSF6, HLA-DRA, VNN1, IRAK4, FAD104,CEACAM1, IFNGR1, FCGR1A, TIFA, CD86 IL1RN, PFKFB3, IL18R1, PRV1, CRTAP,0.83 0.81 0.84 ITGAM, TNFRSF6, IL10alpha, SOCS3, VNN1, BCL2A1, MAPK14,GADD45A, IRAK2, CCL5, ARG2, TLR4, CD86, ANKRD22, TNFSF10, TGFBI HLA-DRA,PRV1, GADD45A, IL1RN, 0.83 0.81 0.84 IL18R1, TNFRSF6, LDLR, IRAK4,BCL2A1, TIFA, PSTPIP2, SOCS3, IL10alpha, FAD104, MKNK1, TNFSF13B, JAK2,TDRD9, TNFSF10, FCGR1A, CD86 INSL3, GADD45A, TGFBI, JAK2, IRAK2, 0.820.82 0.82 OSM, TIFA, TNFSF13B, HLA-DRA, FCGR1A, BCL2A1, PRV1, CEACAM1,SOCS3, MAPK14, IRAK4, ANXA3, TNFRSF6, FAD104, IFNGR1, Gene_MMP9 BCL2A1,ANKRD22, IL10alpha, HLA-DRA, 0.82 0.8 0.84 VNN1, GADD45B, TNFRSF6,CSF1R, IRAK4, ITGAM, IL1RN, IRAK2, LY96, MAPK14, JAK2, Gene_MMP9, TLR4,ARG2, CCL5, SOCS3, MAP2K6 TDRD9, VNN1, GADD45A, ANKRD22, 0.82 0.79 0.85PFKFB3, TNFSF13B, SOCS3, IL18R1, IL1RN, ARG2, CSF1R, HLA-DRA, PRV1,CEACAM1, CD86, IFNGR1, CCL5, MAP2K6, TGFBI, IL10alpha, Gene_MMP9 CRTAP,IL1RN, TIFA, IRAK4, ANXA3, 0.82 0.78 0.86 SOCS3, CD86, CSF1R, FCGR1A,FAD104, ANKRD22, TNFSF13B, PSTPIP2, TDRD9, ARG2, TGFBI, Gene_MMP9, CCL5,IL10alpha, GADD45B, TNFRSF6 ANXA3, TNFRSF6, TDRD9, IRAK2, 0.82 0.78 0.87MAP2K6, INSL3, FCGR1A, GADD45A, NCR1, ARG2, VNN1, PRV1, MAPK14, IRAK4,SOCS3, ITGAM, HLA-DRA, CD86, CEACAM1, LY96, GADD45B VNN1, CCL5, IFNGR1,LY96, IL10alpha, 0.87 0.84 0.89 ITGAM, FCGR1A, FAD104, NCR1, TNFRSF6,TNFSF13B, SOCS3, TIFA, TNFSF10, PSTPIP2, ARG2, IL18R1, CSF1R, OSM,PFKFB3, LDLR, IRAK2 IL18R1, GADD45A, BCL2A1, HLA-DRA, 0.84 0.79 0.88PSTPIP2, ANKRD22, CRTAP, FAD104, CD86, TNFRSF6, Gene_MMP9, IRAK2, SOD2,IL10alpha, IFNGR1, FCGR1A, TIFA, OSM, CCL5, GADD45B, TGFBI, TLR4TNFSF13B, LDLR, GADD45B, MAPK14, 0.84 0.79 0.89 PFKFB3, CRTAP, MAP2K6,NCR1, CCL5, ARG2, SOD2, BCL2A1, MKNK1, TIFA, ANKRD22, Gene_MMP9, TGFBI,IL1RN, HLA-DRA, IL18R1, VNN1, CSF1R TNFRSF6, PSTPIP2, CD86, VNN1, CCL5,0.83 0.85 0.82 MAPK14, TLR4, BCL2A1, ANKRD22, ARG2, ITGAM, IL10alpha,IRAK4, SOCS3, LY96, CRTAP, JAK2, IL1RN, FCGR1A, MAP2K6, TNFSF10, GADD45ATDRD9, CRTAP, ANKRD22, TNFSF13B, 0.83 0.82 0.85 ANXA3, CCL5, FCGR1A,TNFSF10, TNFRSF6, PRV1, IRAK2, CEACAM1, SOCS3, CSF1R, FAD104, PSTPIP2,VNN1, ARG2, IL1RN, HLA-DRA, BCL2A1, INSL3 TNFSF10, TLR4, MAP2K6, PFKFB3,0.83 0.79 0.86 FCGR1A, INSL3, MAPK14, PSTPIP2, IFNGR1, CD86, PRV1,IL10alpha, OSM, FAD104, ITGAM, ANXA3, TIFA, CEACAM1, IL18R1, TNFRSF6,NCR1, GADD45A GADD45B, HLA-DRA, NCR1, TGFBI, OSM, 0.83 0.8 0.86 MKNK1,TLR4, ARG2, CCL5, LDLR, IFNGR1, SOCS3, INSL3, TIFA, TNFSF10, CD86,IL10alpha, GADD45A, CSF1R, TDRD9, BCL2A1, ANXA3 TLR4, ANXA3, IL10alpha,NCR1, JAK2, 0.83 0.76 0.88 TNFSF13B, GADD45A, OSM, SOCS3, CEACAM1,BCL2A1, MKNK1, ARG2, CRTAP, TNFRSF6, Gene_MMP9, PSTPIP2, SOD2, CD86,IL1RN, FCGR1A, CSF1R LY96, TIFA, TLR4, PSTPIP2, Gene_MMP9, 0.83 0.820.83 PRV1, HLA-DRA, CEACAM1, FCGR1A, ARG2, IRAK4, IL1RN, OSM, IFNGR1,TNFSF13B, CSF1R, TDRD9, GADD45B, ANXA3, SOCS3, GADD45A, LDLR INSL3,PSTPIP2, MKNK1, FCGR1A, 0.83 0.82 0.83 PFKFB3, OSM, TGFBI, MAPK14,IRAK2, GADD45A, ANKRD22, CCL5, HLA-DRA, IL10alpha, SOCS3, CD86, IFNGR1,ARG2, Gene_MMP9, GADD45B, VNN1, IL1RN IL1RN, IFNGR1, CCL5, GADD45B,VNN1, 0.83 0.78 0.87 CSF1R, TNFSF10, LDLR, TNFRSF6, INSL3, CD86, OSM,FCGR1A, BCL2A1, CRTAP, TLR4, NCR1, PSTPIP2, SOCS3, MAP2K6, TNFSF13B,Gene_MMP9 ARG2, GADD45B, TNFSF10, IRAK2, 0.82 0.83 0.82 MAPK14, IL1RN,MKNK1, CRTAP, TNFSF13B, PRV1, SOD2, VNN1, IL18R1, HLA-DRA, MAP2K6,INSL3, CEACAM1, IL10alpha, LY96, SOCS3, FCGR1A, ANKRD22 IFNGR1, LDLR,ITGAM, VNN1, IL18R1, 0.82 0.83 0.82 TGFBI, SOCS3, ANKRD22, HLA-DRA,TIFA, OSM, TLR4, IRAK4, INSL3, SOD2, TNFSF13B, LY96, IRAK2, BCL2A1,MAPK14, CCL5, MKNK1 Gene_MMP9, BCL2A1, TDRD9, OSM, 0.82 0.81 0.83MAPK14, IRAK2, CRTAP, MAP2K6, TGFBI, IL18R1, TNFSF10, ANXA3, IFNGR1,GADD45A, TIFA, PSTPIP2, SOCS3, ITGAM, ARG2, HLA-DRA, FAD104, IRAK4IRAK2, IL1RN, ITGAM, LY96, IFNGR1, 0.82 0.81 0.84 TGFBI, TIFA, PFKFB3,Gene_MMP9, FAD104, TNFSF13B, VNN1, LDLR, INSL3, HLA-DRA, NCR1, TDRD9,TNFRSF6, ANXA3, CSF1R, SOCS3, IL18R1 TNFRSF6, INSL3, LDLR, CD86, TGFBI,0.82 0.8 0.84 NCR1, Gene_MMP9, CRTAP, HLA-DRA, BCL2A1, MKNK1, IL18R1,TLR4, CEACAM1, PRV1, CCL5, OSM, TDRD9, PFKFB3, IFNGR1, IRAK2, PSTPIP2PFKFB3, ITGAM, ANKRD22, MAPK14, 0.82 0.8 0.85 TGFBI, PSTPIP2, BCL2A1,IFNGR1, MKNK1, NCR1, ARG2, HLA-DRA, INSL3, CRTAP, FCGR1A, LDLR, CCL5,JAK2, IRAK4, TLR4, LY96, IL10alpha TIFA, IFNGR1, HLA-DRA, Gene_MMP9,0.82 0.8 0.84 PRV1, FAD104, IL10alpha, GADD45B, IRAK4, IL1RN, TDRD9,IL18R1, BCL2A1, CD86, GADD45A, CCL5, ANXA3, OSM, SOCS3, PFKFB3, LDLR,CSF1R FAD104, NCR1, BCL2A1, IRAK2, TLR4, 0.82 0.8 0.84 IL18R1, SOD2,MAPK14, GADD45B, CD86, FCGR1A, CSF1R, OSM, MAP2K6, PFKFB3, LY96, TIFA,MKNK1, PSTPIP2, CRTAP, TGFBI, GADD45A GADD45A, CSF1R, IL18R1, TGFBI,0.85 0.81 0.89 TNFSF13B, ANXA3, OSM, SOCS3, LY96, TDRD9, ITGAM, FCGR1A,IFNGR1, FAD104, HLA-DRA, PSTPIP2, MKNK1, CRTAP, GADD45B, Gene_MMP9,LDLR, TLR4, VNN1 MAP2K6, TGFBI, HLA-DRA, IL10alpha, 0.85 0.82 0.87 VNN1,GADD45B, CEACAM1, PRV1, OSM, IRAK4, IRAK2, ITGAM, CSF1R, TDRD9, NCR1,TNFSF13B, CRTAP, BCL2A1, TIFA, IFNGR1, GADD45A, IL18R1, SOD2 GADD45B,MAPK14, TDRD9, CCL5, OSM, 0.84 0.83 0.85 TNFSF13B, ANXA3, TIFA, ANKRD22,TNFRSF6, TNFSF10, PSTPIP2, TLR4, VNN1, FCGR1A, IL18R1, NCR1, GADD45A,LY96, INSL3, ITGAM, BCL2A1, IRAK2 HLA-DRA, PFKFB3, IRAK4, MKNK1, 0.840.8 0.88 TGFBI, CRTAP, ANXA3, CEACAM1, CCL5, JAK2, TNFSF10, IL1RN,CSF1R, IFNGR1, ARG2, LY96, Gene_MMP9, PRV1, CD86, IRAK2, ITGAM,IL10alpha, OSM FAD104, LY96, NCR1, TLR4, TNFSF13B, 0.84 0.8 0.89 MAPK14,MAP2K6, HLA-DRA, FCGR1A, CD86, ANKRD22, LDLR, IL1RN, IFNGR1, TDRD9,TGFBI, GADD45A, PRV1, PFKFB3, ITGAM, JAK2, PSTPIP2, CRTAP BCL2A1,FCGR1A, CRTAP, Gene_MMP9, 0.84 0.82 0.85 TDRD9, CEACAM1, SOCS3, SOD2,LDLR, GADD45B, LY96, CSF1R, ARG2, TNFRSF6, PSTPIP2, PFKFB3, IL1RN,IL10alpha, VNN1, GADD45A, INSL3, JAK2, IFNGR1 Gene_MMP9, LDLR, CEACAM1,MAPK14, 0.84 0.82 0.86 TLR4, ANXA3, IRAK4, FCGR1A, GADD45B, GADD45A,TGFBI, BCL2A1, CSF1R, PRV1, TNFRSF6, IFNGR1, TDRD9, LY96, MAP2K6, OSM,CRTAP, CD86, FAD104 FCGR1A, ANXA3, MAPK14, TNFRSF6, 0.83 0.82 0.84PSTPIP2, INSL3, ANKRD22, CD86, CRTAP, FAD104, GADD45B, IL18R1, TLR4,IRAK2, ITGAM, JAK2, GADD45A, BCL2A1, IFNGR1, CSF1R, TIFA, NCR1, IRAK4CRTAP, OSM, TNFRSF6, IRAK2, VNN1, 0.83 0.8 0.85 IRAK4, ANXA3, SOD2,ANKRD22, ITGAM, TLR4, MKNK1, IL18R1, CEACAM1, TGFBI, PRV1, Gene_MMP9,TNFSF13B, BCL2A1, HLA-DRA, INSL3, NCR1, CSF1R FAD104, CEACAM1, CCL5,PSTPIP2, 0.83 0.79 0.87 TNFSF10, VNN1, CRTAP, IRAK2, FCGR1A, TNFSF13B,CD86, IL10alpha, ARG2, BCL2A1, IFNGR1, PRV1, IL18R1, TNFRSF6, TIFA,TLR4, JAK2, MAPK14, MAP2K6 ARG2, MAPK14, IRAK4, LDLR, IL10alpha, 0.830.84 0.81 Gene_MMP9, NCR1, OSM, CEACAM1, SOD2, CSF1R, CCL5, GADD45A,ITGAM, BCL2A1, HLA-DRA, PFKFB3, TNFSF13B, TNFSF10, IRAK2, VNN1, JAK2,PRV1 FAD104, IFNGR1, INSL3, PFKFB3, 0.83 0.83 0.83 MAP2K6, LDLR, CD86,ARG2, PRV1, IL1RN, OSM, ITGAM, VNN1, MKNK1, ANXA3, JAK2, GADD45B, CSF1R,TNFSF13B, PSTPIP2, FCGR1A, CRTAP, TGFBI VNN1, SOCS3, ANKRD22, FAD104,IL18R1, 0.83 0.77 0.89 OSM, ITGAM, CCL5, TGFBI, MAPK14, MKNK1, HLA-DRA,LDLR, PSTPIP2, ARG2, CSF1R, IL10alpha, MAP2K6, LY96, FCGR1A, TNFSF10,JAK2, TLR4 CEACAM1, MAP2K6, IL18R1, TIFA, HLA- 0.82 0.82 0.83 DRA,FAD104, TGFBI, LDLR, ANKRD22, IL1RN, SOCS3, TNFSF13B, NCR1, CD86,BCL2A1, IL10alpha, TLR4, CRTAP, MKNK1, ITGAM, JAK2, OSM, ARG2 VNN1,FCGR1A, SOD2, CRTAP, TGFBI, 0.82 0.81 0.84 LDLR, FAD104, NCR1, TNFRSF6,ARG2, GADD45A, OSM, ANXA3, ITGAM, BCL2A1, CSF1R, IFNGR1, TIFA, CEACAM1,CCL5, SOCS3, ANKRD22, Gene_MMP9 CCL5, IL1RN, TIFA, PRV1, TNFSF13B, 0.820.81 0.84 INSL3, IRAK2, MKNK1, MAPK14, FCGR1A, SOCS3, JAK2, FAD104,IFNGR1, CRTAP, IL18R1, GADD45B, SOD2, TNFSF10, HLA-DRA, TNFRSF6,ANKRD22, LDLR PRV1, BCL2A1, SOD2, VNN1, FAD104, 0.82 0.74 0.9 TIFA,IL10alpha, SOCS3, ITGAM, IL18R1, CEACAM1, MAP2K6, TNFSF13B, JAK2, IRAK4,TNFRSF6, OSM, CRTAP, PSTPIP2, TLR4, CSF1R, IL1RN, FCGR1A TNFRSF6,TNFSF10, CD86, IL10alpha, 0.82 0.81 0.83 ARG2, TLR4, JAK2, MAP2K6,GADD45B, LDLR, TIFA, IRAK2, BCL2A1, SOD2, LY96, PFKFB3, HLA-DRA, CSF1R,FAD104, CRTAP, FCGR1A, ANXA3, SOCS3 TNFSF13B, IRAK4, CD86, LDLR, OSM,0.82 0.8 0.83 CCL5, ANXA3, IL1RN, GADD45B, SOCS3, TGFBI, BCL2A1, FAD104,IRAK2, IL10alpha, NCR1, MAP2K6, INSL3, TIFA, CEACAM1, MKNK1, MAPK14,JAK2 LY96, ANXA3, TIFA, CSF1R, GADD45B, 0.85 0.8 0.89 PFKFB3, IL1RN,IL18R1, LDLR, TNFRSF6, OSM, INSL3, CRTAP, MAP2K6, IRAK2, ARG2,IL10alpha, NCR1, FAD104, IRAK4, MKNK1, VNN1, IFNGR1, SOD2 NCR1, IL1RN,PRV1, IL18R1, HLA-DRA, 0.84 0.82 0.86 BCL2A1, GADD45A, FAD104, TLR4,OSM, FCGR1A, TNFSF10, CRTAP, INSL3, GADD45B, LY96, IRAK2, CD86, VNN1,CCL5, JAK2, IL10alpha, MKNK1, IRAK4 GADD45A, MKNK1, ANXA3, TLR4, 0.840.83 0.85 MAP2K6, TIFA, FCGR1A, IRAK2, TDRD9, VNN1, CSF1R, GADD45B,LDLR, IL1RN, ANKRD22, JAK2, HLA-DRA, IL10alpha, PSTPIP2, Gene_MMP9,CRTAP, IL18R1, MAPK14, ARG2 FAD104, IL18R1, IRAK2, TIFA, IL10alpha, 0.830.83 0.84 ITGAM, SOCS3, TDRD9, PSTPIP2, ARG2, INSL3, IL1RN, TLR4,IFNGR1, VNN1, MAPK14, TNFRSF6, SOD2, ANKRD22, NCR1, ANXA3, FCGR1A, CD86,OSM TLR4, TGFBI, CEACAM1, OSM, CRTAP, 0.83 0.83 0.83 IL1RN, TNFRSF6,PRV1, SOD2, MKNK1, VNN1, CSF1R, IL18R1, ANKRD22, MAPK14, ANXA3, TNFSF10,TDRD9, BCL2A1, IRAK4, FCGR1A, CCL5, TNFSF13B, GADD45B ARG2, JAK2, CSF1R,NCR1, LY96, HLA- 0.83 0.82 0.84 DRA, ANXA3, PSTPIP2, IRAK4, BCL2A1,IL1RN, IFNGR1, FCGR1A, VNN1, TNFSF10, MAPK14, TGFBI, GADD45B, INSL3,IRAK2, OSM, CD86, CRTAP, TNFSF13B HLA-DRA, INSL3, PRV1, MAP2K6, TIFA,0.83 0.81 0.84 NCR1, CSF1R, TDRD9, IL18R1, MKNK1, TNFRSF6, TNFSF10,LDLR, IRAK4, FAD104, ITGAM, PSTPIP2, MAPK14, TNFSF13B, GADD45B, CEACAM1,IL1RN, ANXA3, PFKFB3 INSL3, TDRD9, GADD45A, BCL2A1, 0.83 0.78 0.87PFKFB3, TNFRSF6, MAP2K6, GADD45B, TGFBI, IRAK2, CEACAM1, ITGAM,IL10alpha, ANXA3, JAK2, IL1RN, CRTAP, PRV1, SOCS3, TIFA, CCL5, LY96,TNFSF10, OSM VNN1, LDLR, FAD104, HLA-DRA, ARG2, 0.82 0.85 0.8 IFNGR1,IRAK4, TNFRSF6, TIFA, MAP2K6, NCR1, OSM, PRV1, CSF1R, INSL3, TNFSF13B,JAK2, MAPK14, BCL2A1, IRAK2, TLR4, PSTPIP2, TDRD9, ANXA3 ANXA3, TNFSF10,TGFBI, MKNK1, 0.82 0.83 0.82 PSTPIP2, GADD45A, CRTAP, LDLR, INSL3,MAPK14, IFNGR1, BCL2A1, TNFSF13B, GADD45B, Gene_MMP9, IRAK2, CEACAM1,PRV1, SOD2, FAD104, JAK2, NCR1, ARG2, IL1RN TDRD9, LY96, PFKFB3, IRAK2,FAD104, 0.82 0.8 0.85 NCR1, Gene_MMP9, MAPK14, CCL5, LDLR, PSTPIP2, OSM,VNN1, IRAK4, BCL2A1, TIFA, GADD45A, TGFBI, ANKRD22, FCGR1A, IFNGR1,ARG2, CD86, IL18R1 CD86, TNFSF13B, PSTPIP2, IL10alpha, 0.82 0.8 0.84HLA-DRA, MAP2K6, FCGR1A, Gene_MMP9, JAK2, SOCS3, CSF1R, TDRD9, ARG2,NCR1, OSM, FAD104, BCL2A1, TNFRSF6, INSL3, VNN1, ITGAM, PRV1, TLR4,CEACAM1 IL18R1, ARG2, VNN1, TNFRSF6, TIFA, 0.82 0.78 0.86 MKNK1,IL10alpha, CD86, NCR1, OSM, ANKRD22, TDRD9, PSTPIP2, ITGAM, IFNGR1,MAP2K6, BCL2A1, IRAK2, TLR4, LY96, SOCS3, GADD45B, IRAK4, PRV1 TNFSF10,ITGAM, MAP2K6, TIFA, CSF1R, 0.82 0.78 0.86 TDRD9, FAD104, TLR4, GADD45B,HLA- DRA, IRAK2, IRAK4, OSM, FCGR1A, CCL5, SOD2, VNN1, MKNK1, ARG2,Gene_MMP9, TGFBI, TNFSF13B, MAPK14, PFKFB3 TNFSF10, CEACAM1, IFNGR1,TIFA, 0.82 0.75 0.88 MKNK1, ANXA3, IL1RN, IL10alpha, IL18R1, HLA-DRA,SOCS3, Gene_MMP9, MAPK14, TGFBI, JAK2, IRAK2, TLR4, CSF1R, BCL2A1,PSTPIP2, MAP2K6, CD86, ITGAM, SOD2 SOD2, PFKFB3, MAP2K6, HLA-DRA, 0.820.85 0.79 ANKRD22, IL18R1, Gene_MMP9, LDLR, ARG2, GADD45A, JAK2, MKNK1,PRV1, FCGR1A, ITGAM, OSM, NCR1, VNN1, LY96, IFNGR1, TIFA, PSTPIP2,IL1RN, TLR4 CSF1R, FCGR1A, IL18R1, ANKRD22, 0.82 0.81 0.83 MKNK1, NCR1,IRAK2, TDRD9, GADD45A, CRTAP, GADD45B, JAK2, PRV1, SOCS3, CD86, MAPK14,MAP2K6, IFNGR1, LY96, FAD104, OSM, SOD2, TLR4, IL10alpha ARG2, TGFBI,TIFA, IL18R1, TNFRSF6, 0.82 0.8 0.83 CSF1R, CCL5, SOCS3, LY96, MKNK1,BCL2A1, SOD2, FCGR1A, PSTPIP2, GADD45B, IFNGR1, NCR1, TNFSF10, LDLR,PRV1, IL1RN, TDRD9, ANKRD22, TLR4 IL1RN, IL10alpha, IFNGR1, TDRD9, 0.820.8 0.84 PFKFB3, GADD45B, TNFSF10, PSTPIP2, SOCS3, TIFA, MAPK14, CSF1R,TNFSF13B, CRTAP, TNFRSF6, ARG2, IL18R1, LY96, TGFBI, CD86, TLR4,GADD45A, OSM, Gene_MMP9 SOD2, IRAK4, SOCS3, VNN1, IL1RN, 0.86 0.88 0.85ITGAM, TNFSF10, GADD45A, CCL5, CEACAM1, ANKRD22, NCR1, IL18R1, OSM,ARG2, INSL3, MAPK14, MAP2K6, TGFBI, TNFSF13B, PFKFB3, MKNK1, LY96,FCGR1A, CSF1R LDLR, VNN1, GADD45A, SOCS3, TLR4, 0.85 0.85 0.85 SOD2,BCL2A1, IL18R1, IRAK2, HLA-DRA, TIFA, CEACAM1, OSM, INSL3, TNFSF13B,TNFRSF6, Gene_MMP9, CRTAP, ARG2, LY96, GADD45B, CSF1R, FCGR1A, IL1RN,PFKFB3 ARG2, PRV1, TNFSF10, FAD104, SOD2, 0.85 0.84 0.86 ANXA3, IL18R1,JAK2, LDLR, OSM, IFNGR1, PSTPIP2, TNFRSF6, IRAK4, IL1RN, VNN1, FCGR1A,ITGAM, IL10alpha, IRAK2, INSL3, CD86, TDRD9, TIFA, MKNK1 GADD45B, IRAK2,MAPK14, Gene_MMP9, 0.85 0.81 0.89 CD86, CEACAM1, SOD2, SOCS3, ARG2,ANXA3, LDLR, JAK2, VNN1, IFNGR1, FAD104, NCR1, PRV1, OSM, TDRD9, MKNK1,ITGAM, INSL3, IL1RN, ANKRD22, CCL5 IL10alpha, IRAK2, HLA-DRA, Gene_MMP9,0.84 0.85 0.84 TGFBI, LDLR, TIFA, GADD45A, ARG2, CSF1R, MAP2K6, CEACAM1,PRV1, OSM, CD86, TNFRSF6, LY96, FAD104, PSTPIP2, ANXA3, IFNGR1, NCR1,CCL5, GADD45B, PFKFB3 GADD45A, SOCS3, SOD2, TGFBI, HLA- 0.84 0.84 0.85DRA, VNN1, CD86, CCL5, BCL2A1, CRTAP, MAP2K6, PRV1, IL18R1, CSF1R, OSM,IRAK2, PSTPIP2, TLR4, FCGR1A, ANKRD22, CEACAM1, JAK2, INSL3, TDRD9,TNFSF10 FCGR1A, TLR4, ANKRD22, CEACAM1, 0.84 0.78 0.9 IRAK4, LY96,TDRD9, ARG2, CRTAP, ANXA3, LDLR, MAPK14, CD86, Gene_MMP9, INSL3,GADD45B, TNFSF10, VNN1, IRAK2, PSTPIP2, TIFA, TNFRSF6, TGFBI, IL18R1,IL1RN SOCS3, VNN1, FCGR1A, SOD2, OSM, 0.84 0.83 0.84 TNFSF10, LY96,Gene_MMP9, GADD45B, CRTAP, PRV1, HLA-DRA, GADD45A, TLR4, ARG2, IRAK2,FAD104, INSL3, PSTPIP2, TIFA, TGFBI, IL18R1, MAP2K6, LDLR, ANXA3 MAP2K6,LDLR, TIFA, TNFSF13B, IL18R1, 0.84 0.83 0.84 ITGAM, SOCS3, OSM, ANXA3,GADD45A, Gene_MMP9, CD86, IL1RN, IFNGR1, PRV1, FCGR1A, MAPK14, CCL5,VNN1, ARG2, PSTPIP2, IRAK2, NCR1, TDRD9, TNFRSF6 TIFA, ANKRD22,TNFSF13B, SOCS3, 0.84 0.82 0.86 NCR1, IRAK4, JAK2, GADD45A, CCL5, LDLR,MAPK14, IL18R1, SOD2, TGFBI, CSF1R, IFNGR1, MAP2K6, TNFSF10, IRAK2,LY96, IL1RN, TNFRSF6, VNN1, INSL3, PFKFB3 MAP2K6, FAD104, CCL5, IL18R1,NCR1, 0.84 0.81 0.86 VNN1, IL10alpha, ANKRD22, IFNGR1, MAPK14, CD86,MKNK1, TLR4, LY96, TIFA, PSTPIP2, TNFRSF6, LDLR, CSF1R, ARG2, TGFBI,JAK2, PFKFB3, OSM, TDRD9 JAK2, OSM, IRAK4, VNN1, SOCS3, 0.83 0.84 0.83GADD45B, IL1RN, FCGR1A, TNFRSF6, Gene_MMP9, ANKRD22, ARG2, IL10alpha,CCL5, IL18R1, ANXA3, LY96, PSTPIP2, TIFA, TNFSF10, FAD104, MAP2K6,MKNK1, PFKFB3, CRTAP TIFA, IRAK2, ANKRD22, CCL5, IL10alpha, 0.83 0.850.82 INSL3, CEACAM1, TLR4, FCGR1A, NCR1, CD86, BCL2A1, GADD45A, ITGAM,MAP2K6, CRTAP, VNN1, TDRD9, SOCS3, ANXA3, TNFSF10, LY96, MKNK1, JAK2,ARG2 PSTPIP2, CEACAM1, FAD104, TIFA, 0.83 0.82 0.84 ANKRD22, OSM,TNFSF13B, IRAK4, INSL3, GADD45A, IL10alpha, CSF1R, HLA- DRA, SOCS3,GADD45B, CCL5, Gene_MMP9, LY96, TLR4, IFNGR1, TGFBI, BCL2A1, MAP2K6,CD86, PFKFB3 IL1RN, JAK2, PFKFB3, OSM, CD86, IL18R1, 0.83 0.82 0.84SOD2, GADD45B, ITGAM, TNFRSF6, MAP2K6, LDLR, TLR4, TIFA, INSL3, SOCS3,IFNGR1, ANKRD22, GADD45A, IRAK4, CRTAP, CSF1R, TNFSF13B, PRV1, PSTPIP2IFNGR1, VNN1, ANKRD22, FCGR1A, JAK2, 0.83 0.78 0.87 MAP2K6, SOD2,TNFSF13B, IRAK4, CEACAM1, LY96, MAPK14, INSL3, NCR1, Gene_MMP9, CCL5,HLA-DRA, LDLR, TNFRSF6, PFKFB3, ANXA3, SOCS3, ARG2, ITGAM, CSF1R LDLR,GADD45A, IFNGR1, ARG2, 0.83 0.83 0.82 MAPK14, HLA-DRA, CRTAP, OSM,TDRD9, CSF1R, FCGR1A, Gene_MMP9, NCR1, PRV1, IRAK4, TGFBI, TLR4, LY96,IL1RN, FAD104, SOD2, CCL5, TNFRSF6, MAP2K6, TNFSF13B IL18R1, IL1RN,IRAK4, CEACAM1, 0.83 0.83 0.83 ITGAM, LY96, ANKRD22, ARG2, TDRD9, LDLR,NCR1, IL10alpha, ANXA3, CD86, MAPK14, TNFRSF6, SOD2, MKNK1, GADD45B,CRTAP, PFKFB3, CSF1R, INSL3, PSTPIP2, CCL5 PSTPIP2, NCR1, MKNK1, SOCS3,IL1RN, 0.83 0.81 0.84 IFNGR1, IL18R1, CSF1R, ITGAM, LDLR, TIFA, CRTAP,OSM, TLR4, CEACAM1, Gene_MMP9, INSL3, MAP2K6, CCL5, FAD104, HLA-DRA,PRV1, VNN1, PFKFB3, JAK2

In some embodiments, the methods or kits respectively described orreferenced in Section 5.2 and Section 5.3 use any one of the biomarkersets listed in Table M. The biomarker sets listed in Table M wereidentified in the computational experiments described in Section 6.14.2,below, in which 1600 random subcombinations of the biomarkers listed inTable K were tested. Table M lists some of the biomarker sets thatprovided high accuracy scores against the validation populationdescribed in Section 6.14.2. Each row of Table M lists a singlebiomarker set that can be used in the methods and kits respectivelyreferenced in Sections 5.2 and 5.3. In other words, each row of Table Mdescribes a biomarker set that can be used to discriminate betweensepsis and SIRS subjects (e.g., to determine whether a subject is likelyto acquire SEPSIS). In some embodiments, nucleic acid forms of thebiomarkers listed in Table M are used in the methods and kitsrespectively referenced in Sections 5.2 and 5.3. In some embodiments,protein forms of the biomarkers listed in Table M are used. In somehybrid embodiments, some of the biomarkers in a biomarker set from TableM are in protein form and some of the biomarkers in the same biomarkerset from Table M are in nucleic acid form in the methods and kitsrespectively referenced in Sections 5.2 and 5.3.

In some embodiments, a given biomarker set listed in Table M is usedwith the addition of one, two, three, four, five, six, seven, eight, ornine or more additional biomarkers from Table I that are not within thegiven set of biomarkers from Table M. In some embodiments, a given setof biomarkers from Table M is used with the addition of one, two, three,four, five, six, seven, eight, or nine or more additional biomarkersfrom any one of Table I, 30, 31, 32, 33, 34, or 36 that are not withinthe given biomarker set from Table M. In Table M, accuracy, specificity,and senstitivity are described with reference to T⁻¹² time point datadescribed in Section 6.14.2, below.

TABLE M Exemplary sets of biomarkers used in the methods or kitsreferenced in Sections 5.2 and 5.3 BIOMARKER SET ACCURACY SPECIFICITYSENSISTIVITY ALPHAFETOPROTEIN, IL6, IL8 0.78 0.76 0.8 CREACTIVEPROTEIN,TIMP1, IL6 0.78 0.75 0.8 PROTEIN_MMP9, IL8, IL6 0.77 0.8 0.74 IL8, IL6,IL10 0.77 0.72 0.81 CREACTIVEPROTEIN, IL6, 0.77 0.77 0.76 PROTEIN_MMP9APOLIPOPROTEINCIII, IL8, IL6 0.76 0.74 0.78 IL6, IL8, CREACTIVEPROTEIN0.76 0.74 0.79 ALPHAFETOPROTEIN, MCP1, IL10, IL6 0.8 0.8 0.8ALPHAFETOPROTEIN, IL10, IL6, 0.79 0.7 0.86 PROTEIN_MMP9ALPHAFETOPROTEIN, 0.78 0.74 0.81 PROTEIN_MMP9, IL6, APOLIPOPROTEINCIIIAPOLIPOPROTEINCIII, IL6, 0.78 0.73 0.81 BETA2MICROGLOBULIN, TIMP1 IL6,BETA2MICROGLOBULIN, IL10, 0.77 0.73 0.81 APOLIPOPROTEINCIII IL6,PROTEIN_MMP9, IL10, MCP1 0.77 0.81 0.73 APOLIPOPROTEINCIII, 0.77 0.780.75 ALPHAFETOPROTEIN, PROTEIN_MMP9, IL6 IL10, TIMP1, IL6,ALPHAFETOPROTEIN 0.77 0.71 0.83 TIMP1, IL6, CREACTIVEPROTEIN, 0.76 0.80.73 BETA2MICROGLOBULIN PROTEIN_MMP9, CREACTIVEPROTEIN, 0.8 0.78 0.81MCP1, IL10, IL6 APOLIPOPROTEINCIII, 0.79 0.81 0.78 CREACTIVEPROTEIN,IL10, ALPHAFETOPROTEIN, IL6 CREACTIVEPROTEIN, 0.79 0.77 0.81ALPHAFETOPROTEIN, IL6, PROTEIN_MMP9, IL8 IL6, TIMP1, MCP1, 0.78 0.750.82 APOLIPOPROTEINCIII, CREACTIVEPROTEIN CREACTIVEPROTEIN, 0.78 0.790.76 APOLIPOPROTEINCIII, TIMP1, IL8, PROTEIN_MMP9 CREACTIVEPROTEIN,IL10, MCP1, IL6, 0.77 0.78 0.77 TIMP1 IL10, IL8, APOLIPOPROTEINCIII,IL6, 0.77 0.73 0.8 TIMP1 IL10, CREACTIVEPROTEIN, MCP1, IL6, 0.77 0.720.82 APOLIPOPROTEINCIII IL6, ALPHAFETOPROTEIN, IL8, 0.77 0.75 0.78CREACTIVEPROTEIN, TIMP1 TIMP1, MCP1, PROTEIN_MMP9, IL6, 0.8 0.81 0.79APOLIPOPROTEINCIII, CREACTIVEPROTEIN TIMP1, IL6, IL10, CREACTIVEPROTEIN,0.79 0.77 0.8 APOLIPOPROTEINCIII, PROTEIN_MMP9 MCP1, PROTEIN_MMP9, 0.790.75 0.82 APOLIPOPROTEINCIII, IL6, TIMP1, IL10 IL10, CREACTIVEPROTEIN,IL6, 0.78 0.78 0.79 ALPHAFETOPROTEIN, TIMP1, PROTEIN_MMP9 PROTEIN_MMP9,CREACTIVEPROTEIN, 0.78 0.77 0.79 ALPHAFETOPROTEIN, IL10, IL6, MCP1 IL6,MCP1, IL10, TIMP1, 0.78 0.76 0.79 APOLIPOPROTEINCIII, IL8 TIMP1, IL6,IL10, 0.77 0.72 0.83 BETA2MICROGLOBULIN, PROTEIN_MMP9,APOLIPOPROTEINCIII IL10, MCP1, ALPHAFETOPROTEIN, 0.77 0.76 0.78APOLIPOPROTEINCIII, IL6, PROTEIN_MMP9 BETA2MICROGLOBULIN, IL6, TIMP1,0.77 0.74 0.79 ALPHAFETOPROTEIN, CREACTIVEPROTEIN, PROTEIN_MMP9 MCP1,IL10, IL8, IL6, TIMP1, 0.79 0.77 0.81 PROTEIN_MMP9, CREACTIVEPROTEINPROTEIN_MMP9, 0.79 0.77 0.8 BETA2MICROGLOBULIN, APOLIPOPROTEINCIII, IL8,IL6, ALPHAFETOPROTEIN, CREACTIVEPROTEIN IL8, MCP1, CREACTIVEPROTEIN,0.79 0.71 0.85 APOLIPOPROTEINCIII, ALPHAFETOPROTEIN, PROTEIN_MMP9, IL6TIMP1, IL6, CREACTIVEPROTEIN, 0.78 0.76 0.8 APOLIPOPROTEINCIII,PROTEIN_MMP9, IL8, ALPHAFETOPROTEIN IL10, IL6, BETA2MICROGLOBULIN, 0.780.7 0.85 CREACTIVEPROTEIN, APOLIPOPROTEINCIII, MCP1, IL8APOLIPOPROTEINCIII, 0.78 0.8 0.76 PROTEIN_MMP9, MCP1, IL6,ALPHAFETOPROTEIN, IL10, TIMP1 IL10, CREACTIVEPROTEIN, 0.78 0.74 0.82ALPHAFETOPROTEIN, BETA2MICROGLOBULIN, IL8, PROTEIN_MMP9,APOLIPOPROTEINCIII TIMP1, IL10, CREACTIVEPROTEIN, 0.78 0.81 0.74APOLIPOPROTEINCIII, IL6, IL8, MCP1 IL8, TIMP1, CREACTIVEPROTEIN, IL6,0.78 0.8 0.76 IL10, BETA2MICROGLOBULIN, APOLIPOPROTEINCIIIAPOLIPOPROTEINCIII, 0.78 0.78 0.77 CREACTIVEPROTEIN, IL8, IL10,PROTEIN_MMP9, IL6, BETA2MICROGLOBULIN TIMP1, MCP1, IL10, 0.8 0.74 0.86BETA2MICROGLOBULIN, PROTEIN_MMP9, IL6, ALPHAFETOPROTEIN,APOLIPOPROTEINCIII TIMP1, PROTEIN_MMP9, IL6, 0.79 0.77 0.82ALPHAFETOPROTEIN, IL10, APOLIPOPROTEINCIII, MCP1, IL8 IL10, IL6, MCP1,CREACTIVEPROTEIN, 0.79 0.79 0.79 APOLIPOPROTEINCIII, PROTEIN_MMP9,BETA2MICROGLOBULIN, ALPHAFETOPROTEIN TIMP1, MCP1, IL10, 0.79 0.76 0.81CREACTIVEPROTEIN, ALPHAFETOPROTEIN, IL6, PROTEIN_MMP9, IL8APOLIPOPROTEINCIII, 0.79 0.73 0.83 ALPHAFETOPROTEIN, TIMP1,BETA2MICROGLOBULIN, MCP1, IL10, IL6, IL8 CREACTIVEPROTEIN, TIMP1, 0.780.78 0.79 APOLIPOPROTEINCIII, MCP1, IL6, ALPHAFETOPROTEIN,BETA2MICROGLOBULIN, PROTEIN_MMP9 BETA2MICROGLOBULIN, IL10, IL8, 0.780.73 0.83 APOLIPOPROTEINCIII, PROTEIN_MMP9, IL6, TIMP1, CREACTIVEPROTEINAPOLIPOPROTEINCIII, IL8, 0.78 0.78 0.77 ALPHAFETOPROTEIN, IL6,PROTEIN_MMP9, IL10, TIMP1, MCP1 APOLIPOPROTEINCIII, IL6, IL8, 0.78 0.710.83 PROTEIN_MMP9, TIMP1, BETA2MICROGLOBULIN, IL10, CREACTIVEPROTEINAPOLIPOPROTEINCIII, MCP1, IL10, 0.77 0.76 0.78 PROTEIN_MMP9, TIMP1,ALPHAFETOPROTEIN, CREACTIVEPROTEIN, IL6 PROTEIN_MMP9, CREACTIVEPROTEIN,0.79 0.78 0.81 IL6, TIMP1, BETA2MICROGLOBULIN, IL10, APOLIPOPROTEINCIII,MCP1, ALPHAFETOPROTEIN APOLIPOPROTEINCIII, 0.79 0.77 0.81 PROTEIN_MMP9,ALPHAFETOPROTEIN, CREACTIVEPROTEIN, IL6, IL10, IL8, TIMP1,BETA2MICROGLOBULIN ALPHAFETOPROTEIN, TIMP1, 0.79 0.79 0.79 PROTEIN_MMP9,MCP1, IL6, APOLIPOPROTEINCIII, BETA2MICROGLOBULIN, IL10,CREACTIVEPROTEIN APOLIPOPROTEINCIII, 0.79 0.78 0.79 PROTEIN_MMP9, MCP1,BETA2MICROGLOBULIN, IL8, IL6, IL10, CREACTIVEPROTEIN, TIMP1 TIMP1,APOLIPOPROTEINCIII, IL6, 0.79 0.72 0.84 CREACTIVEPROTEIN, MCP1,PROTEIN_MMP9, IL8, BETA2MICROGLOBULIN, ALPHAFETOPROTEINBETA2MICROGLOBULIN, IL8, 0.78 0.77 0.79 CREACTIVEPROTEIN, TIMP1, IL6,ALPHAFETOPROTEIN, APOLIPOPROTEINCIII, PROTEIN_MMP9, IL10 IL6, IL8,TIMP1, PROTEIN_MMP9, IL10, 0.78 0.73 0.83 BETA2MICROGLOBULIN,APOLIPOPROTEINCIII, CREACTIVEPROTEIN, ALPHAFETOPROTEIN PROTEIN_MMP9,IL10, MCP1, 0.78 0.8 0.75 CREACTIVEPROTEIN, ALPHAFETOPROTEIN, IL6,TIMP1, APOLIPOPROTEINCIII, BETA2MICROGLOBULIN IL10, IL8,ALPHAFETOPROTEIN, IL6, 0.78 0.79 0.76 TIMP1, PROTEIN_MMP9, MCP1,BETA2MICROGLOBULIN, CREACTIVEPROTEIN ALPHAFETOPROTEIN, MCP1, IL6, 0.780.78 0.78 BETA2MICROGLOBULIN, PROTEIN_MMP9, CREACTIVEPROTEIN, TIMP1,APOLIPOPROTEINCIII, IL10 TIMP1, IL6, CREACTIVEPROTEIN, 0.79 0.78 0.81ALPHAFETOPROTEIN, IL10, BETA2MICROGLOBULIN, MCP1, APOLIPOPROTEINCIII,IL8, PROTEIN_MMP9 IL8, CREACTIVEPROTEIN, TIMP1, IL10, 0.79 0.78 0.8MCP1, IL6, ALPHAFETOPROTEIN, PROTEIN_MMP9, APOLIPOPROTEINCIII,BETA2MICROGLOBULIN MCP1, TIMP1, APOLIPOPROTEINCIII, 0.78 0.8 0.77ALPHAFETOPROTEIN, PROTEIN_MMP9, IL10, CREACTIVEPROTEIN,BETA2MICROGLOBULIN, IL8, IL6 BETA2MICROGLOBULIN, MCP1, IL6, 0.78 0.780.79 CREACTIVEPROTEIN, IL10, IL8, ALPHAFETOPROTEIN, APOLIPOPROTEINCIII,TIMP1, PROTEIN_MMP9 CREACTIVEPROTEIN, TIMP1, IL10, IL6, 0.78 0.76 0.8ALPHAFETOPROTEIN, APOLIPOPROTEINCIII, IL8, BETA2MICROGLOBULIN, MCP1,PROTEIN_MMP9 MCP1, TIMP1, IL6, 0.78 0.78 0.78 ALPHAFETOPROTEIN,PROTEIN_MMP9, BETA2MICROGLOBULIN, APOLIPOPROTEINCIII, CREACTIVEPROTEIN,IL8, IL10 ALPHAFETOPROTEIN, 0.78 0.8 0.75 APOLIPOPROTEINCIII,PROTEIN_MMP9, BETA2MICROGLOBULIN, IL10, TIMP1, MCP1, IL6, IL8,CREACTIVEPROTEIN TIMP1, IL10, BETA2MICROGLOBULIN, 0.78 0.76 0.8 IL8,APOLIPOPROTEINCIII, IL6, MCP1, CREACTIVEPROTEIN, ALPHAFETOPROTEIN,PROTEIN_MMP9 BETA2MICROGLOBULIN, 0.77 0.74 0.8 ALPHAFETOPROTEIN, MCP1,IL10, APOLIPOPROTEINCIII, TIMP1, CREACTIVEPROTEIN, IL8, PROTEIN_MMP9,IL6 IL8, MCP1, BETA2MICROGLOBULIN, 0.77 0.79 0.75 PROTEIN_MMP9, IL10,TIMP1, IL6, CREACTIVEPROTEIN, ALPHAFETOPROTEIN, APOLIPOPROTEINCIII

In some embodiments, the methods or kits respectively described orreferenced in Section 5.2 and Section 5.3 use any one of the subsets ofbiomarkers listed in Table N. The subsets of biomarkers listed in TableN were identified in the computational experiments described in Section6.14.5, below, in which 4600 random subcombinations of the biomarkerslisted in Table I were tested. Table N lists some of the biomarker setsthat provided high accuracy scores against the validation populationdescribed in Section 6.14.5. Each row of Table N lists a single set ofbiomarkers that can be used in the methods and kits respectivelyreferenced in Sections 5.2 and 5.3. In other words, each row of Table Ndescribes a set of biomarkers that can be used to discriminate betweensepsis and SIRS subjects. In some embodiments, nucleic acid forms of thebiomarkers listed in Table N are used in the methods and kitsrespectively referenced in Sections 5.2 and 5.3. In some embodiments,protein forms of the biomarkers listed in Table N are used. In someembodiments, some of the biomarkers in a biomarker set from Table N arein protein form and some of the biomarkers in the same biomarker setfrom Table N are in nucleic acid form in the methods and kitsrespectively referenced in Sections 5.2 and 5.3.

In some embodiments, a given set of biomarkers from Table N is used withthe addition of one, two, three, four, five, six, seven, eight, or nineor more additional biomarkers from from any one of Table 30, 31, 32, 33,34, or 36 that are not within the given set of biomarkers from Table N.In Table N, accuracy, specificity, and senstitivity are described withreference to T⁻¹² time point data described in Section 6.14.5, below.

TABLE N Exemplary sets of biomarkers used in the methods or kitsreferenced in Sections 5.2 and 5.3 BIOMARKER SET ACCURACY SPECIFICITYSENSISTIVITY TLR4, ARG2, OSM 0.85 0.83 0.88 IRAK4, OSM, TNFSF10 0.830.79 0.87 PSTPIP2, SOCS3, TIMP1 0.82 0.81 0.83 FCGR1A, IL6, MAP2K6 0.810.84 0.79 SOCS3, TNFSF10, NCR1 0.81 0.73 0.87 IL8, IL18R1,Beta2Microglobulin 0.81 0.79 0.82 OSM, NCR1, IL8 0.81 0.77 0.83 PFKFB3,MKNK1, FCGR1A 0.8 0.79 0.81 TIMP1, IL18R1, ARG2 0.8 0.78 0.83 FCGR1A,MAP2K6, IRAK4 0.8 0.75 0.86 Gene_MMP9, IL8, GADD45B 0.8 0.75 0.84 INSL3,ANKRD22, MAP2K6, LDLR 0.87 0.83 0.9 PSTPIP2, ARG2, CRTAP, GADD45A 0.830.81 0.85 CEACAM1, GADD45B, GADD45A, OSM 0.83 0.75 0.91 OSM, CSF1R,IL10, ANKRD22 0.83 0.88 0.78 TIMP1, ARG2, GADD45B, VNN1 0.83 0.83 0.82HLA-DRA, PSTPIP2, INSL3, MKNK1 0.83 0.79 0.86 CD86, TGFBI, ANKRD22,SOCS3 0.82 0.82 0.83 GADD45A, PSTPIP2, GADD45B, IL18R1 0.82 0.76 0.86ANKRD22, MAP2K6, Protein_MMP9, 0.81 0.8 0.82 FAD104 IFNGR1, FAD104,CSF1R, IL8 0.81 0.78 0.84 OSM, TDRD9, ARG2, HLA-DRA 0.81 0.77 0.85ANKRD22, CReactiveProtein, OSM, IL10 0.81 0.76 0.85 TDRD9, TNFSF13B,CReactiveProtein, 0.81 0.76 0.85 MAP2K6 TNFSF10, Gene_MMP9, IL8, FCGR1A0.8 0.79 0.81 IL10, NCR1, IL6, INSL3 0.8 0.79 0.81 CD86, FCGR1A, BCL2A1,LY96 0.8 0.79 0.81 IL8, VNN1, IL6, GADD45B 0.8 0.79 0.82 HLA-DRA,TNFSF10, OSM, MKNK1 0.8 0.76 0.84 PFKFB3, INSL3, IL10alpha, FCGR1A 0.80.76 0.84 TNFSF10, IRAK4, OSM, ARG2, MAPK14 0.85 0.84 0.86 CD86,CEACAM1, IL18R1, GADD45B, 0.83 0.85 0.81 CCL5 MCP1, CSF1R, GADD45B,Protein_MMP9, 0.83 0.83 0.82 Beta2Microglobulin IL8, CD86, IRAK2, IL1RN,TIFA 0.82 0.84 0.81 IRAK4, OSM, INSL3, CEACAM1, 0.82 0.82 0.82 TNFSF13BCReactiveProtein, SOCS3, HLA-DRA, 0.82 0.76 0.88 GADD45B, OSM CD86,NCR1, PRV1, IL1RN, GADD45B 0.82 0.77 0.86 TNFRSF6, ITGAM, PSTPIP2, ARG2,0.82 0.77 0.87 BCL2A1 IRAK4, LDLR, OSM, PSTPIP2, GADD45A 0.81 0.81 0.82Gene_MMP9, SOD2, PFKFB3, ARG2, 0.81 0.78 0.84 CD86 CReactiveProtein,IL18R1, NCR1, CD86, 0.81 0.78 0.84 GADD45A IL8, IL18R1, LDLR, SOD2,PSTPIP2 0.81 0.77 0.84 Gene_MMP9, CSF1R, TGFBI, MAP2K6, 0.81 0.8 0.81ANKRD22 CReactiveProtein, LDLR, IRAK2, OSM, 0.81 0.8 0.82 PSTPIP2 ITGAM,SOCS3, IL8, ARG2, JAK2 0.81 0.79 0.83 TNFSF10, LY96, IL10alpha, IL10,OSM 0.8 0.83 0.78 GADD45B, IL6, INSL3, ANKRD22, IL8 0.8 0.81 0.8 CSF1R,IL6, IL1RN, TLR4, JAK2 0.8 0.79 0.81 TDRD9, OSM, ITGAM, ANKRD22, 0.80.73 0.87 Gene_MMP9 IL8, TNFRSF6, CReactiveProtein, IRAK4, 0.8 0.79 0.81PRV1 OSM, IL1RN, JAK2, GADD45B, CSF1R 0.8 0.78 0.82 CD86,Beta2Microglobulin, PFKFB3, 0.8 0.78 0.82 TNFSF13B, TNFRSF6 MAPK14,TGFBI, GADD45A, ANKRD22, 0.8 0.75 0.85 CReactiveProtein MKNK1, CD86,OSM, TIFA, HLA-DRA, 0.85 0.79 0.89 SOCS3 CD86, CEACAM1, LDLR, NCR1, 0.830.81 0.84 AlphaFetoprotein, IRAK2 INSL3, PRV1, LY96, Protein_MMP9, IL8,0.82 0.82 0.82 OSM FAD104, ARG2, FCGR1A, SOCS3, HLA- 0.82 0.8 0.84 DRA,ANXA3 CCL5, TIMP1, ARG2, IL6, IFNGR1, SOD2 0.82 0.77 0.87 CRTAP, OSM,GADD45B, TNFSF10, 0.82 0.75 0.88 MKNK1, TGFBI LDLR, OSM, IL6, JAK2,INSL3, FCGR1A 0.82 0.82 0.81 Beta2Microglobulin, FAD104, TGFBI, 0.820.82 0.82 NCR1, ARG2, GADD45B CSF1R, VNN1, MAP2K6, LY96, OSM, 0.82 0.810.82 Beta2Microglobulin ApolipoproteinCIII, HLA-DRA, GADD45A, 0.82 0.80.83 ITGAM, TNFRSF6, MAP2K6 PRV1, TGFBI, VNN1, GADD45B, IL1RN, 0.81 0.80.82 CSF1R IRAK4, TIMP1, ANKRD22, GADD45B, 0.81 0.78 0.83 OSM, TLR4SOD2, MKNK1, MCP1, OSM, TIFA, 0.81 0.77 0.84 SOCS3 FAD104, TGFBI, ANXA3,IL18R1, PRV1, 0.81 0.77 0.85 IL10alpha FCGR1A, IL8, Beta2Microglobulin,0.81 0.83 0.79 GADD45B, ANKRD22, TNFSF10 TIFA, Beta2Microglobulin,IL18R1, 0.81 0.8 0.81 CRTAP, IL6, TGFBI CD86, IL10, MCP1, TIMP1, OSM,ANXA3 0.81 0.79 0.82 INSL3, FAD104, TGFBI, CEACAM1, 0.81 0.77 0.85CSF1R, PFKFB3 PRV1, IL8, TNFSF10, FCGR1A, IFNGR1, 0.81 0.77 0.84CReactiveProtein ANKRD22, BCL2A1, CRTAP, NCR1, 0.81 0.72 0.88 SOCS3,IL18R1 INSL3, IRAK2, CD86, JAK2, IL10, 0.8 0.84 0.77 FAD104 MCP1,PSTPIP2, AlphaFetoprotein, 0.8 0.81 0.8 CReactiveProtein, IL6,ApolipoproteinCIII CSF1R, OSM, IFNGR1, TDRD9, 0.8 0.8 0.8 Gene_MMP9,FCGR1A TIMP1, IFNGR1, TNFSF10, GADD45A, 0.8 0.8 0.81 BCL2A1, SOD2FCGR1A, MKNK1, CRTAP, LDLR, 0.8 0.79 0.81 Gene_MMP9, Beta2MicroglobulinITGAM, AlphaFetoprotein, FCGR1A, 0.8 0.78 0.82 MCP1, MKNK1, GADD45AMCP1, FCGR1A, OSM, PFKFB3, FAD104, 0.8 0.77 0.82 TDRD9 TNFSF10,Gene_MMP9, FCGR1A, 0.86 0.85 0.86 AlphaFetoprotein, INSL3, CSF1R, IL8OSM, Beta2Microglobulin, ANKRD22, 0.84 0.85 0.83 CSF1R, GADD45B,TNFRSF6, ApolipoproteinCIII BCL2A1, TDRD9, OSM, PRV1, IRAK2, 0.84 0.830.85 TLR4, MAPK14 LDLR, OSM, ApolipoproteinCIII, IL6, 0.83 0.83 0.83TIMP1, ARG2, TNFRSF6 IL1RN, TNFSF13B, AlphaFetoprotein, 0.83 0.8 0.85MCP1, ANKRD22, ARG2, OSM NCR1, ARG2, PSTPIP2, GADD45A, LY96, 0.83 0.810.84 OSM, BCL2A1 FCGR1A, TNFSF13B, INSL3, TIFA, 0.83 0.79 0.87ApolipoproteinCIII, ITGAM, CD86 LY96, CReactiveProtein, FCGR1A, 0.820.85 0.8 Beta2Microglobulin, IL8, OSM, VNN1 PSTPIP2, ARG2, IRAK2,TNFSF13B, 0.82 0.85 0.8 GADD45A, IL8, CRTAP MCP1, SOCS3, HLA-DRA, 0.820.84 0.81 ApolipoproteinCIII, IL10alpha, GADD45A, MAP2K6 IL18R1, MAPK14,Gene_MMP9, TIFA, 0.82 0.75 0.89 FCGR1A, SOCS3, MKNK1 Beta2Microglobulin,CRTAP, ARG2, 0.82 0.82 0.82 ANKRD22, TNFRSF6, IRAK4, OSM PFKFB3, IRAK2,IRAK4, OSM, JAK2, 0.82 0.82 0.82 Beta2Microglobulin, CEACAM1 TIFA,CRTAP, PFKFB3, JAK2, IL6, 0.82 0.82 0.82 TGFBI, CD86 GADD45B, Gene_MMP9,TNFSF13B, 0.82 0.76 0.88 IRAK2, VNN1, TIFA, SOCS3 INSL3, IL6, CD86,IL10alpha, 0.82 0.86 0.78 CReactiveProtein, TGFBI, ITGAM GADD45B, MCP1,INSL3, 0.82 0.81 0.82 CReactiveProtein, ARG2, CCL5, SOCS3 TNFSF10, IL8,ApolipoproteinCIII, TGFBI, 0.82 0.8 0.83 CSF1R, OSM, SOD2 PRV1, PSTPIP2,ARG2, TIMP1, 0.82 0.79 0.84 Protein_MMP9, IL6, SOD2 CD86, LY96, MAP2K6,IL6, IL10, IRAK2, 0.81 0.78 0.84 TNFSF10 BCL2A1, MCP1, ARG2, SOCS3,NCR1, 0.81 0.8 0.81 IL10, LY96 SOCS3, ApolipoproteinCIII, NCR1, 0.81 0.80.82 CEACAM1, ANKRD22, FCGR1A, IL6 INSL3, TNFSF10, SOD2, FCGR1A, 0.810.77 0.85 PSTPIP2, IL10, IL8 FCGR1A, OSM, Protein_MMP9, 0.81 0.76 0.84GADD45A, PSTPIP2, ARG2, Gene_MMP9 TIMP1, SOCS3, LY96, CSF1R, 0.81 0.840.77 CReactiveProtein, CCL5, TNFSF13B ANKRD22, CEACAM1, TLR4, 0.81 0.820.8 ApolipoproteinCIII, SOCS3, ITGAM, IL10 INSL3, IRAK2, FCGR1A, MAP2K6,0.81 0.81 0.8 CRTAP, ITGAM, CSF1R VNN1, SOCS3, Beta2Microglobulin, 0.810.81 0.81 MAP2K6, IL6, ANKRD22, IL10 TNFSF10, TGFBI, CReactiveProtein,0.81 0.8 0.82 Beta2Microglobulin, TNFRSF6, ARG2, PRV1 IL18R1, IL6,CRTAP, IRAK4, GADD45A, 0.8 0.81 0.79 Protein_MMP9, TNFSF13BAlphaFetoprotein, ARG2, NCR1, PSTPIP2, 0.8 0.8 0.81 ApolipoproteinCIII,CD86, GADD45B ANKRD22, TIFA, JAK2, IL10, IL6, CCL5, 0.8 0.79 0.82 CSF1RPRV1, TNFSF13B, TLR4, OSM, ARG2, 0.8 0.78 0.82 AlphaFetoprotein, HLA-DRACSF1R, TLR4, SOD2, FCGR1A, CRTAP, 0.8 0.78 0.83 TNFSF13B, GADD45A JAK2,IRAK2, ITGAM, IL6, MKNK1, 0.8 0.77 0.83 Gene_MMP9, FCGR1A GADD45B, PRV1,CSF1R, NCR1, CD86, 0.8 0.76 0.83 MKNK1, JAK2 Beta2Microglobulin,TNFSF10, IL18R1, 0.8 0.75 0.85 GADD45B, Protein_MMP9, FAD104, PSTPIP2MAPK14, TIFA, ITGAM, MKNK1, CSF1R, 0.8 0.73 0.86 IRAK4, Protein_MMP9TIFA, TNFSF13B, LY96, GADD45B, IL6, 0.8 0.8 0.8 INSL3, OSM IL6, GADD45B,CEACAM1, IRAK4, 0.8 0.8 0.8 TGFBI, INSL3, TNFSF13B GADD45B, ARG2,IL18R1, ANKRD22, 0.8 0.8 0.8 AlphaFetoprotein, IL10, PSTPIP2 TNFSF13B,IFNGR1, OSM, FAD104, 0.8 0.8 0.8 CSF1R, PSTPIP2, TIFA TDRD9, ITGAM,TNFSF10, ANXA3, 0.8 0.8 0.8 ApolipoproteinCIII, MCP1, INSL3 SOCS3,Protein_MMP9, SOD2, LY96, 0.8 0.78 0.82 ARG2, IRAK2, OSM CEACAM1, IL10,TNFRSF6, IL18R1, 0.8 0.78 0.82 ARG2, FCGR1A, CReactiveProtein CCL5,FCGR1A, CReactiveProtein, 0.8 0.74 0.86 ApolipoproteinCIII, IL18R1,Protein_MMP9, ITGAM NCR1, SOD2, IRAK2, IL8, OSM, HLA- 0.86 0.89 0.84DRA, ARG2, GADD45A PFKFB3, PSTPIP2, GADD45B, INSL3, 0.85 0.86 0.84FAD104, TNFRSF6, ARG2, IL10alpha CSF1R, CEACAM1, GADD45B, OSM, 0.84 0.860.82 LDLR, MCP1, ARG2, AlphaFetoprotein TIFA, NCR1, BCL2A1, OSM, CCL5,TLR4, 0.82 0.82 0.82 CD86, CEACAM1 FAD104, LDLR, INSL3, IRAK4, LY96,0.82 0.81 0.83 TLR4, GADD45B, TIMP1 FAD104, JAK2, TNFSF13B, ARG2, 0.820.79 0.85 CReactiveProtein, IL10alpha, TLR4, PRV1 CRTAP, LY96, TDRD9,Gene_MMP9, 0.82 0.79 0.85 HLA-DRA, SOCS3, IL8, Protein_MMP9 CRTAP,GADD45B, TIFA, 0.81 0.83 0.8 ApolipoproteinCIII, LY96, IL8, GADD45A,MKNK1 IL8, CSF1R, ARG2, TGFBI, PRV1, 0.81 0.82 0.81 TNFRSF6, CEACAM1,JAK2 ARG2, Beta2Microglobulin, GADD45A, 0.81 0.79 0.83 IL6, INSL3, IL8,JAK2, TIMP1 SOD2, IL10, IL8, ARG2, PSTPIP2, INSL3, 0.81 0.77 0.86 CSF1R,ANXA3 CD86, IL6, BCL2A1, CCL5, GADD45B, 0.81 0.86 0.76 IRAK4, LDLR, ARG2ANXA3, MAP2K6, VNN1, GADD45A, 0.81 0.81 0.81 CSF1R, FAD104, IL6, IRAK2IL8, GADD45A, TDRD9, 0.81 0.8 0.82 Beta2Microglobulin, ANKRD22, GADD45B,PRV1, CSF1R IRAK4, PRV1, GADD45A, IL8, 0.81 0.79 0.82 TNFSF13B, CD86,FCGR1A, TIMP1 IRAK4, MAPK14, GADD45B, HLA-DRA, 0.81 0.77 0.84 JAK2,PRV1, SOD2, IL6 IL18R1, IL6, GADD45B, MAPK14, IL10, 0.81 0.76 0.86 JAK2,IL8, ANKRD22 TLR4, IRAK2, TNFRSF6, TGFBI, IL8, 0.81 0.88 0.74 ARG2,GADD45B, GADD45A ANXA3, IFNGR1, SOCS3, VNN1, TIFA, 0.81 0.8 0.81CReactiveProtein, IRAK4, AlphaFetoprotein ANKRD22, CCL5, TGFBI, CEACAM1,0.81 0.8 0.82 CD86, Gene_MMP9, IFNGR1, GADD45B NCR1, ARG2, TIMP1,Beta2Microglobulin, 0.81 0.77 0.84 ANXA3, TIFA, BCL2A1, MAP2K6 OSM,TIMP1, IL1RN, IL8, BCL2A1, 0.81 0.76 0.84 IFNGR1, CSF1R, CD86 IL10,NCR1, IRAK2, IL18R1, ARG2, 0.81 0.76 0.84 PSTPIP2, Gene_MMP9, LY96IL18R1, TNFSF10, SOCS3, 0.81 0.74 0.88 ApolipoproteinCIII, HLA-DRA,GADD45A, Beta2Microglobulin, ARG2 IL10alpha, IL10, MKNK1, LY96, OSM, 0.80.8 0.81 JAK2, IFNGR1, CEACAM1 HLA-DRA, TIMP1, OSM, CD86, NCR1, 0.8 0.780.82 IL1RN, TNFSF10, FAD104 CSF1R, TNFSF13B, ANKRD22, IFNGR1, 0.8 0.770.82 Protein_MMP9, PFKFB3, NCR1, TGFBI ANXA3, IL10alpha, PSTPIP2, CSF1R,0.8 0.77 0.83 IL1RN, FAD104, CD86, CReactiveProtein CSF1R, ANKRD22,TGFBI, IRAK4, 0.8 0.77 0.83 Protein_MMP9, TIMP1, HLA-DRA, PFKFB3TNFSF13B, JAK2, ARG2, CCL5, IL18R1, 0.8 0.76 0.85 GADD45B, CD86, GADD45AGADD45B, BCL2A1, IL1RN, FCGR1A, 0.8 0.76 0.84 MAPK14, SOCS3, ITGAM, PRV1IL6, JAK2, CReactiveProtein, MCP1, 0.8 0.85 0.76 TIMP1, BCL2A1, GADD45B,LY96 AlphaFetoprotein, ApolipoproteinCIII, 0.8 0.84 0.77 CEACAM1, CRTAP,IL18R1, NCR1, TIMP1, TGFBI ANXA3, FAD104, MKNK1, 0.8 0.82 0.78CReactiveProtein, AlphaFetoprotein, CSF1R, IRAK4, IL6 TIMP1, VNN1, TIFA,CCL5, TDRD9, 0.8 0.82 0.78 FCGR1A, ApolipoproteinCIII, IL1RN TNFSF10,TLR4, JAK2, OSM, 0.8 0.8 0.81 Beta2Microglobulin, ITGAM, IL1RN, HLA- DRAApolipoproteinCIII, MAPK14, IRAK2, 0.8 0.79 0.81 TNFSF13B, GADD45B,SOCS3, CEACAM1, TNFSF10 IL1RN, ANKRD22, FCGR1A, GADD45A, 0.8 0.79 0.81TGFBI, CSF1R, MCP1, MAPK14 SOCS3, HLA-DRA, IRAK2, 0.8 0.79 0.81Protein_MMP9, MAP2K6, INSL3, CReactiveProtein, Gene_MMP9 INSL3, BCL2A1,ARG2, GADD45B, 0.8 0.78 0.82 MAPK14, ITGAM, IRAK2, LDLR GADD45B,Beta2Microglobulin, 0.8 0.77 0.82 Protein_MMP9, IFNGR1, IRAK2, PSTPIP2,IL8, FCGR1A CSF1R, HLA-DRA, IRAK4, FAD104, 0.84 0.88 0.82 CRTAP, MCP1,GADD45B, CCL5, IL6 HLA-DRA, Gene_MMP9, FAD104, IRAK2, 0.84 0.85 0.83TNFRSF6, LY96, CReactiveProtein, FCGR1A, CD86 GADD45A, GADD45B, OSM,ARG2, 0.84 0.81 0.86 FAD104, MAPK14, IRAK2, ITGAM, MKNK1 IL6, IL18R1,IL1RN, HLA-DRA, CD86, 0.83 0.88 0.8 IRAK2, NCR1, TNFSF10, CCL5 CD86,IRAK2, ARG2, PFKFB3, MAPK14, 0.83 0.85 0.82 PRV1, VNN1, HLA-DRA, FAD104TDRD9, FCGR1A, ARG2, 0.83 0.8 0.86 AlphaFetoprotein, JAK2,ApolipoproteinCIII, TIMP1, MAP2K6, CCL5 PFKFB3, CReactiveProtein, TDRD9,OSM, 0.83 0.85 0.81 IFNGR1, CCL5, TIMP1, ARG2, ITGAM NCR1, CSF1R,MAP2K6, INSL3, IFNGR1, 0.83 0.85 0.81 FAD104, IL6, ARG2, IL18R1 PSTPIP2,OSM, LDLR, Protein_MMP9, 0.83 0.82 0.84 LY96, TNFSF13B, ANXA3, IL1RN,Beta2Microglobulin FAD104, NCR1, VNN1, IRAK2, 0.83 0.81 0.84ApolipoproteinCIII, IL10alpha, LDLR, FCGR1A, IRAK4 IL10alpha, HLA-DRA,TGFBI, FCGR1A, 0.82 0.84 0.81 CSF1R, IRAK2, GADD45A, PFKFB3, SOCS3 IL8,IRAK4, CSF1R, IL18R1, 0.82 0.83 0.81 AlphaFetoprotein, IL1RN, BCL2A1,TNFSF13B, INSL3 SOCS3, MAP2K6, PSTPIP2, OSM, 0.82 0.82 0.82 MAPK14,MKNK1, ApolipoproteinCIII, IL18R1, TLR4 TIMP1, IL8, PFKFB3, CD86, SOCS3,0.82 0.81 0.83 JAK2, IRAK2, IL10alpha, Protein_MMP9 TIMP1, INSL3,TNFRSF6, PFKFB3, CD86, 0.82 0.8 0.85 JAK2, IL8, CRTAP, Protein_MMP9IRAK4, MAPK14, ApolipoproteinCIII, IL6, 0.82 0.88 0.76 MAP2K6, MCP1,TIFA, ARG2, CD86 TLR4, IL10alpha, IL8, GADD45A, IRAK2, 0.82 0.82 0.81MAP2K6, MCP1, HLA-DRA, MAPK14 LY96, MCP1, CD86, VNN1, OSM, ARG2, 0.810.83 0.8 TDRD9, CCL5, INSL3 CCL5, CRTAP, ApolipoproteinCIII, 0.81 0.810.81 Gene_MMP9, IFNGR1, TNFSF13B, ANKRD22, GADD45A, OSM MCP1, IL6,FCGR1A, PSTPIP2, VNN1, 0.81 0.79 0.83 TNFSF10, TIMP1, Protein_MMP9,CReactiveProtein PFKFB3, IL6, NCR1, MAP2K6, FAD104, 0.81 0.79 0.84 CD86,TLR4, TDRD9, OSM ApolipoproteinCIII, CReactiveProtein, 0.81 0.77 0.86TGFBI, MKNK1, PRV1, FAD104, HLA- DRA, ARG2, TIMP1 IL10alpha, IL10,ANXA3, IL6, CSF1R, 0.81 0.82 0.8 TGFBI, PSTPIP2, IL8, INSL3 IL6, IRAK2,CReactiveProtein, CCL5, 0.81 0.8 0.82 ANKRD22, MCP1, GADD45A, PFKFB3,IL10alpha TIFA, IL1RN, IL6, ITGAM, 0.81 0.79 0.82 CReactiveProtein,CCL5, TGFBI, IL10, NCR1 CEACAM1, IFNGR1, TNFSF10, INSL3, 0.81 0.78 0.84BCL2A1, Beta2Microglobulin, IL10, ARG2, SOCS3 SOCS3, LDLR, SOD2, FAD104,MAP2K6, 0.81 0.75 0.86 PSTPIP2, GADD45B, IRAK4, GADD45A MKNK1, IL8,TNFSF13B, FAD104, 0.81 0.83 0.78 ITGAM, GADD45B, NCR1, IL18R1,ApolipoproteinCIII IL8, Gene_MMP9, TNFSF10, MKNK1, 0.81 0.82 0.79 MCP1,IL6, CCL5, ApolipoproteinCIII, SOD2 NCR1, PFKFB3, ApolipoproteinCIII,0.81 0.78 0.83 INSL3, OSM, VNN1, AlphaFetoprotein, TNFSF10, CRTAPFCGR1A, CReactiveProtein, PRV1, NCR1, 0.81 0.75 0.86 ARG2, INSL3, IL10,TGFBI, MAPK14 IL8, IRAK2, PFKFB3, CEACAM1, TIFA, 0.8 0.8 0.81Protein_MMP9, IRAK4, CRTAP, TDRD9 ARG2, INSL3, CSF1R, TNFSF13B, 0.8 0.790.82 Beta2Microglobulin, PRV1, FCGR1A, GADD45B, CRTAP GADD45A, IL8,TIMP1, CReactiveProtein, 0.8 0.77 0.84 MAP2K6, TGFBI, CRTAP, TNFRSF6,BCL2A1 HLA-DRA, ApolipoproteinCIII, INSL3, 0.8 0.77 0.84 FAD104, TIMP1,IRAK4, FCGR1A, IL6, GADD45A ARG2, JAK2, IL1RN, VNN1, IRAK4, 0.8 0.840.77 CSF1R, ANKRD22, BCL2A1, TDRD9 CReactiveProtein, PFKFB3, CD86,IL1RN, 0.8 0.8 0.8 TLR4, Beta2Microglobulin, IRAK2, TNFSF10, TNFRSF6GADD45B, MAP2K6, JAK2, MAPK14, 0.8 0.79 0.81 TIMP1, IRAK4,CReactiveProtein, TLR4, TGFBI JAK2, TLR4, CCL5, IL6, CReactiveProtein,0.8 0.73 0.87 IFNGR1, ApolipoproteinCIII, GADD45B, NCR1 CSF1R, TNFRSF6,INSL3, MKNK1, IL8, 0.86 0.85 0.86 MAP2K6, FAD104, NCR1, IL1RN, MCP1 IL8,PRV1, SOCS3, IRAK2, ARG2, 0.85 0.86 0.83 IL10alpha, NCR1, CCL5,CReactiveProtein, MKNK1 TNFSF13B, TLR4, ARG2, IL6, SOCS3, 0.84 0.8 0.88Beta2Microglobulin, FAD104, MCP1, HLA- DRA, PSTPIP2 IL6, MCP1,Beta2Microglobulin, IL1RN, 0.84 0.82 0.86 TDRD9, IFNGR1,ApolipoproteinCIII, FCGR1A, OSM, IL8 FCGR1A, IL6, LY96, LDLR, IL18R1,0.84 0.81 0.87 CSF1R, CCL5, NCR1, TNFRSF6, IRAK4 IL6, TGFBI, IL18R1,ANXA3, IL1RN, 0.83 0.84 0.82 GADD45B, ANKRD22, LDLR, TLR4, CEACAM1MAPK14, IL6, CSF1R, IL1RN, ITGAM, 0.83 0.86 0.79 Beta2Microglobulin,MAP2K6, IL10, PSTPIP2, FAD104 CReactiveProtein, FCGR1A, CCL5, 0.83 0.860.8 ApolipoproteinCIII, OSM, IRAK2, GADD45A, CRTAP, PFKFB3, ITGAMTNFSF10, AlphaFetoprotein, CCL5, IL8, 0.82 0.79 0.85 IRAK4, OSM,IL10alpha, ARG2, CReactiveProtein, TIFA TDRD9, TNFSF10, GADD45B, 0.820.85 0.78 CReactiveProtein, IL8, ARG2, ANXA3, TGFBI, IL1RN, CCL5IL10alpha, ANXA3, TNFSF10, IL1RN, 0.82 0.85 0.78 TGFBI, FAD104, INSL3,MAP2K6, MAPK14, ApolipoproteinCIII TIMP1, Beta2Microglobulin, ITGAM,0.82 0.82 0.81 LDLR, MCP1, IL8, FCGR1A, TIFA, IL10alpha, MAP2K6 TNFRSF6,TGFBI, JAK2, SOD2, ANXA3, 0.82 0.82 0.81 VNN1, CCL5, INSL3, CSF1R, IL10TDRD9, IL10alpha, MAPK14, NCR1, 0.82 0.8 0.83 LY96, GADD45B, IRAK2,CReactiveProtein, INSL3, ITGAM LDLR, JAK2, IFNGR1, IRAK2, SOCS3, 0.820.79 0.84 ITGAM, Protein_MMP9, INSL3, ApolipoproteinCIII, CEACAM1 CSF1R,Beta2Microglobulin, IRAK4, 0.82 0.78 0.85 MKNK1, PRV1, TNFRSF6, PSTPIP2,IL18R1, HLA-DRA, CCL5 BCL2A1, TLR4, IL8, TIMP1, SOD2, 0.82 0.77 0.86CReactiveProtein, CRTAP, ApolipoproteinCIII, GADD45B, FAD104 ARG2, OSM,TNFSF13B, CReactiveProtein, 0.81 0.86 0.77 AlphaFetoprotein, IL6, CRTAP,Beta2Microglobulin, MCP1, TDRD9 FAD104, TNFSF13B, IL1RN, GADD45B, 0.810.82 0.8 IFNGR1, IL18R1, TNFRSF6, MCP1, PRV1, IL8 IL8, ITGAM, CSF1R,TNFRSF6, INSL3, 0.81 0.82 0.81 IL10alpha, IFNGR1, IL10, IL1RN, SOD2MCP1, IFNGR1, TNFRSF6, MAPK14, 0.81 0.78 0.84 FAD104, IL18R1, IRAK4,INSL3, IL10alpha, Beta2Microglobulin NCR1, PRV1, Protein_MMP9, TIMP1,0.81 0.74 0.88 ANKRD22, INSL3, CD86, CCL5, MKNK1, Gene_MMP9 NCR1, INSL3,CEACAM1, FAD104, 0.81 0.86 0.76 IL10alpha, TIFA, TNFSF13B, IL6, CCL5,CReactiveProtein CRTAP, IL1RN, IL18R1, FAD104, NCR1, 0.81 0.8 0.82HLA-DRA, TGFBI, LY96, IL6, IRAK4 OSM, NCR1, IL8, GADD45B, 0.81 0.8 0.82Protein_MMP9, TNFRSF6, TNFSF13B, Beta2Microglobulin, IL1RN, IRAK2 CD86,IL10alpha, CSF1R, IRAK2, 0.81 0.79 0.83 ANKRD22, OSM, AlphaFetoprotein,Gene_MMP9, IL10, IRAK4 ARG2, IRAK4, GADD45A, VNN1, IL18R1, 0.81 0.790.83 JAK2, ANXA3, CSF1R, HLA-DRA, PFKFB3 LY96, TDRD9, NCR1, TNFRSF6,CSF1R, 0.81 0.76 0.86 PRV1, IL18R1, ARG2, Beta2Microglobulin, IL10alphaINSL3, TDRD9, CRTAP, TNFRSF6, 0.81 0.75 0.88 IRAK4, SOD2, LDLR, ANKRD22,OSM, CSF1R FAD104, PFKFB3, IL18R1, IL10, MAPK14, 0.81 0.83 0.79 ARG2,CD86, IL1RN, CCL5, GADD45A TNFSF10, CSF1R, TNFSF13B, MKNK1, 0.81 0.810.8 ITGAM, PFKFB3, AlphaFetoprotein, SOCS3, TNFRSF6, FAD104 CSF1R,PFKFB3, ApolipoproteinCIII, 0.81 0.81 0.81 TLR4, ARG2, PRV1, ANKRD22,ITGAM, TIFA, TNFRSF6 TGFBI, IL10, TDRD9, PFKFB3, INSL3, 0.81 0.8 0.81CSF1R, PSTPIP2, MKNK1, NCR1, HLA- DRA ANKRD22, TIMP1, CRTAP, HLA-DRA,0.81 0.78 0.83 ApolipoproteinCIII, CD86, TNFRSF6, Gene_MMP9, VNN1, IL10ARG2, NCR1, IRAK4, FCGR1A, FAD104, 0.81 0.77 0.84 TNFRSF6, PFKFB3,MAP2K6, TGFBI, MKNK1 TLR4, ANKRD22, IL10alpha, VNN1, 0.81 0.76 0.84Protein_MMP9, TNFRSF6, ARG2, TNFSF10, OSM, FCGR1A FAD104, PRV1,Protein_MMP9, IL10alpha, 0.81 0.75 0.86 ARG2, TNFSF13B, FCGR1A, CEACAM1,CCL5, IL1RN TNFRSF6, IL6, TGFBI, PSTPIP2, ANXA3, 0.8 0.83 0.78 ANKRD22,ApolipoproteinCIII, OSM, SOCS3, MAPK14 IL8, OSM, IRAK4, TDRD9, LDLR, 0.80.81 0.8 TNFSF13B, IL10, IFNGR1, ARG2, SOD2 PSTPIP2, BCL2A1, CD86,ANXA3, 0.8 0.84 0.77 IL10alpha, SOD2, OSM, INSL3, TNFSF13B, GADD45B IL6,ANXA3, SOCS3, MAP2K6, TGFBI, 0.8 0.83 0.77 ANKRD22, CRTAP, BCL2A1, CCL5,TLR4 HLA-DRA, CSF1R, TGFBI, MAP2K6, 0.8 0.82 0.78 BCL2A1, CD86, TLR4,IL1RN, IL6, ApolipoproteinCIII ApolipoproteinCIII, CCL5, SOCS3, TIMP1,0.8 0.81 0.79 Gene_MMP9, AlphaFetoprotein, ITGAM, INSL3, CEACAM1, LDLRIL8, TNFRSF6, IL6, IL1RN, PSTPIP2, 0.8 0.79 0.81 ApolipoproteinCIII,CD86, JAK2, TLR4, Protein_MMP9 IL10alpha, JAK2, MCP1, CEACAM1, 0.8 0.780.82 ApolipoproteinCIII, BCL2A1, PRV1, Protein_MMP9, MAP2K6, IFNGR1FCGR1A, LY96, JAK2, GADD45B, LDLR, 0.8 0.77 0.82 IL6, VNN1, MCP1,Gene_MMP9, SOD2 CSF1R, TNFRSF6, INSL3, MKNK1, IL8, 0.86 0.85 0.86MAP2K6, FAD104, NCR1, IL1RN, MCP1 IL8, PRV1, SOCS3, IRAK2, ARG2, 0.850.86 0.83 IL10alpha, NCR1, CCL5, CReactiveProtein, MKNK1 LDLR, CD86,NCR1, IRAK4, IL18R1, 0.85 0.84 0.87 Protein_MMP9, PRV1, GADD45B, ARG2,LY96, AlphaFetoprotein MAP2K6, CD86, INSL3, 0.85 0.81 0.88ApolipoproteinCIII, IL8, OSM, TNFSF13B, IL1RN, BCL2A1, FAD104, GADD45ANCR1, GADD45B, TNFSF10, IL10alpha, 0.84 0.87 0.82 FAD104, LY96, IL6,IL10, ARG2, CReactiveProtein, TGFBI CD86, CEACAM1, INSL3, PFKFB3, 0.830.86 0.81 IL10alpha, FAD104, SOD2, Gene_MMP9, SOCS3, ApolipoproteinCIII,FCGR1A SOCS3, ARG2, ApolipoproteinCIII, IRAK4, 0.83 0.84 0.82 PFKFB3,IFNGR1, NCR1, IL8, CReactiveProtein, VNN1, TDRD9 ARG2, OSM,CReactiveProtein, SOD2, 0.83 0.85 0.81 CEACAM1, FCGR1A, TIMP1, IL10,IL18R1, ANKRD22, IRAK2 TGFBI, SOD2, IL10, CD86, CEACAM1, 0.83 0.83 0.83TDRD9, IRAK4, ANXA3, LDLR, OSM, ARG2 CReactiveProtein, IL10alpha, TIMP1,LY96, 0.83 0.83 0.83 IL8, SOD2, MAP2K6, MAPK14, TLR4, PSTPIP2, INSL3ARG2, PSTPIP2, SOD2, INSL3, FAD104, 0.83 0.8 0.86 JAK2, TIFA, PFKFB3,IRAK2, IL6, ANXA3 PSTPIP2, CEACAM1, GADD45A, 0.83 0.84 0.82ApolipoproteinCIII, ITGAM, PRV1, TLR4, IL10alpha, ARG2, SOCS3, NCR1 OSM,SOCS3, CSF1R, IRAK2, VNN1, IL6, 0.83 0.82 0.83 SOD2, LDLR, BCL2A1,ANKRD22, CD86 CRTAP, LDLR, TGFBI, INSL3, TIFA, 0.83 0.82 0.83 FAD104,AlphaFetoprotein, IL8, JAK2, IRAK4, BCL2A1 CD86, ITGAM, PSTPIP2, IL18R1,IL6, 0.83 0.77 0.87 IFNGR1, GADD45B, IL10, Beta2Microglobulin, FCGR1A,FAD104 PRV1, Beta2Microglobulin, IL1RN, NCR1, 0.82 0.84 0.81 CSF1R,IFNGR1, TIMP1, SOCS3, LDLR, TIFA, ARG2 IL10alpha, GADD45A, LDLR, SOCS3,0.82 0.83 0.81 MAP2K6, LY96, CSF1R, Protein_MMP9, MCP1, TDRD9, IL8CSF1R, TDRD9, TIMP1, SOD2, FCGR1A, 0.82 0.83 0.82 IFNGR1, INSL3, CD86,TNFRSF6, HLA- DRA, MAP2K6 IL8, IL18R1, BCL2A1, MKNK1, 0.82 0.82 0.83CReactiveProtein, CCL5, IL6, SOCS3, FCGR1A, PSTPIP2, ApolipoproteinCIIIANXA3, IL6, CD86, SOD2, CEACAM1, 0.82 0.8 0.85 FCGR1A, ANKRD22, NCR1,PSTPIP2, IL8, MAPK14 Protein_MMP9, TNFRSF6, ITGAM, 0.82 0.79 0.85 CSF1R,INSL3, TIFA, BCL2A1, IL1RN, TGFBI, FCGR1A, ApolipoproteinCIII ANKRD22,IL10alpha, SOCS3, IRAK4, 0.82 0.76 0.88 OSM, INSL3, TGFBI, MCP1, IL8,TNFSF13B, PRV1 ANKRD22, LDLR, VNN1, TIMP1, IRAK2, 0.82 0.85 0.8IL10alpha, GADD45B, ARG2, MAPK14, CSF1R, TNFRSF6 TIFA, ARG2, TNFSF10,INSL3, CD86, IL8, 0.82 0.82 0.82 IRAK2, OSM, CSF1R, HLA-DRA, ITGAMANKRD22, TIFA, PSTPIP2, CCL5, 0.82 0.79 0.84 Gene_MMP9,Beta2Microglobulin, NCR1, FCGR1A, INSL3, SOCS3, IL10alphaApolipoproteinCIII, AlphaFetoprotein, 0.82 0.82 0.81 NCR1, CCL5,GADD45A, IL18R1, JAK2, TDRD9, OSM, TLR4, Gene_MMP9 CReactiveProtein,IL18R1, TGFBI, 0.82 0.8 0.83 TNFSF10, MAP2K6, LDLR, FAD104, ARG2,HLA-DRA, GADD45B, ANXA3 IL18R1, IRAK4, LY96, INSL3, TNFRSF6, 0.82 0.80.83 CReactiveProtein, CD86, GADD45B, CRTAP, IL8, MAPK14 IL8, FCGR1A,CSF1R, VNN1, IL10alpha, 0.81 0.85 0.78 PSTPIP2, IL6, IL1RN, TLR4,GADD45B, LY96 HLA-DRA, IL6, FAD104, GADD45A, 0.81 0.79 0.84 INSL3,ITGAM, CSF1R, IFNGR1, Protein_MMP9, SOCS3, NCR1 IL10, LDLR,AlphaFetoprotein, IL1RN, 0.81 0.79 0.84 INSL3, ApolipoproteinCIII,PSTPIP2, CCL5, SOD2, TGFBI, VNN1 Protein_MMP9, IL10, TGFBI, INSL3, 0.810.84 0.78 IRAK2, TNFRSF6, IL8, PSTPIP2, OSM, AlphaFetoprotein, NCR1 IL8,TLR4, MCP1, ApolipoproteinCIII, 0.81 0.83 0.79 Beta2Microglobulin, IL6,IL10, VNN1, CD86, PSTPIP2, ITGAM FAD104, GADD45A, SOCS3, PSTPIP2, 0.810.81 0.81 IL6, TGFBI, TIMP1, HLA-DRA, TNFSF10, IL10alpha, MKNK1 PRV1,IL8, FCGR1A, GADD45A, IRAK2, 0.81 0.81 0.81 VNN1, CD86, IL18R1,Protein_MMP9, MAP2K6, ITGAM CRTAP, JAK2, IRAK2, CEACAM1, PRV1, 0.81 0.790.82 CCL5, SOD2, BCL2A1, SOCS3, IL1RN, ApolipoproteinCIII MCP1, CCL5,HLA-DRA, IRAK4, OSM, 0.81 0.76 0.85 LDLR, PFKFB3, CReactiveProtein,MKNK1, GADD45A, LY96 ARG2, INSL3, IL6, ITGAM, TGFBI, LDLR, 0.81 0.820.79 IL10, CD86, IL8, TNFSF13B, IL10alpha Protein_MMP9, CD86, GADD45B,LY96, 0.81 0.82 0.8 SOD2, FCGR1A, IL8, AlphaFetoprotein, CSF1R, FAD104,CRTAP TNFSF13B, ApolipoproteinCIII, LDLR, 0.81 0.77 0.84 TDRD9, CEACAM1,AlphaFetoprotein, IRAK4, INSL3, GADD45A, CRTAP, IFNGR1 MKNK1, PSTPIP2,Beta2Microglobulin, 0.8 0.82 0.79 ANKRD22, TIFA, IL10alpha, TGFBI,AlphaFetoprotein, NCR1, PRV1, SOCS3 TIFA, MKNK1, IL6, ANXA3, FAD104, 0.80.82 0.79 PSTPIP2, TNFSF13B, LDLR, INSL3, SOD2, JAK2 TNFSF13B, IFNGR1,IL18R1, CD86, 0.8 0.81 0.8 Beta2Microglobulin, TGFBI, CSF1R,CReactiveProtein, CRTAP, MCP1, JAK2 TNFSF13B, Beta2Microglobulin, CSF1R,0.8 0.79 0.82 JAK2, CRTAP, IL1RN, IL10, SOCS3, ANKRD22, PFKFB3, LDLRTNFRSF6, OSM, PRV1, INSL3, TLR4, 0.8 0.77 0.83 MKNK1, IRAK4, HLA-DRA,VNN1, IL10alpha, FCGR1A IL8, ApolipoproteinCIII, GADD45B, 0.8 0.77 0.83SOCS3, ARG2, TNFSF13B, IL1RN, CCL5, ANXA3, CReactiveProtein, TIFATNFSF13B, SOCS3, Protein_MMP9, SOD2, 0.8 0.76 0.86 TNFRSF6, NCR1,FAD104, IL6, OSM, CCL5, TDRD9 CD86, INSL3, ANXA3, GADD45B, VNN1, 0.80.84 0.77 IFNGR1, IL6, PFKFB3, PSTPIP2, Beta2Microglobulin, IRAK2 IL6,Gene_MMP9, FAD104, TIFA, TGFBI, 0.8 0.82 0.78 Beta2Microglobulin,IL10alpha, ANXA3, IL18R1, NCR1, INSL3 CEACAM1, JAK2, MCP1, OSM, IL18R1,0.8 0.82 0.78 MKNK1, ANKRD22, TLR4, CSF1R, PSTPIP2, IL1RN CSF1R,AlphaFetoprotein, HLA-DRA, 0.8 0.8 0.8 TDRD9, ITGAM, SOCS3, FCGR1A,IRAK2, TIFA, TNFSF10, Protein_MMP9 CSF1R, IL10alpha, TNFRSF6, TNFSF13B,0.8 0.79 0.81 LDLR, INSL3, AlphaFetoprotein, IL10, TIFA, VNN1, HLA-DRAIL18R1, MCP1, ANKRD22, TGFBI, ARG2, 0.8 0.77 0.83 ANXA3, GADD45A, IL1RN,TNFRSF6, PSTPIP2, IRAK2 IL10alpha, IFNGR1, MAPK14, FCGR1A, 0.8 0.76 0.83Gene_MMP9, GADD45A, VNN1, ANKRD22, TNFSF13B, CCL5, IRAK2 IL8,CReactiveProtein, CSF1R, TLR4, 0.85 0.85 0.85 TNFRSF6, Gene_MMP9, TDRD9,OSM, PFKFB3, IFNGR1, ApolipoproteinCIII, PSTPIP2 JAK2, OSM, GADD45B,MCP1, IL1RN, 0.85 0.81 0.88 ANKRD22, IL18R1, Gene_MMP9, ITGAM, NCR1,ApolipoproteinCIII, PFKFB3 TNFSF10, MKNK1, PFKFB3, ANXA3, 0.84 0.84 0.84CRTAP, CD86, MAPK14, IL8, OSM, GADD45B, HLA-DRA, INSL3 IL1RN,AlphaFetoprotein, ARG2, MAP2K6, 0.83 0.86 0.82 CEACAM1, GADD45B, CRTAP,ANXA3, INSL3, ApolipoproteinCIII, NCR1, FAD104 IL6, LDLR, TDRD9,TNFRSF6, NCR1, 0.83 0.84 0.83 ITGAM, AlphaFetoprotein, FCGR1A, ARG2,TNFSF10, OSM, BCL2A1 IL10, SOD2, GADD45A, TNFSF13B, 0.83 0.86 0.81IRAK4, LY96, HLA-DRA, PSTPIP2, IL6, IFNGR1, ARG2, LDLR CCL5, CSF1R,LDLR, GADD45A, INSL3, 0.83 0.8 0.86 JAK2, AlphaFetoprotein, OSM,Beta2Microglobulin, PRV1, HLA-DRA, MKNK1 ANKRD22, TNFSF13B, TIMP1, VNN1,0.83 0.83 0.83 IRAK4, FCGR1A, CEACAM1, IRAK2, ARG2, ANXA3, CD86, IL1RNJAK2, AlphaFetoprotein, IL1RN, SOCS3, 0.82 0.83 0.82 ANKRD22, IL10alpha,IL8, TGFBI, CD86, IL10, CSF1R, CReactiveProtein VNN1, GADD45B, MAP2K6,TNFSF13B, 0.82 0.79 0.85 IRAK2, TLR4, CReactiveProtein, PSTPIP2, MCP1,CSF1R, IL8, TDRD9 SOD2, IL10, CReactiveProtein, 0.82 0.77 0.86ApolipoproteinCIII, Beta2Microglobulin, IFNGR1, OSM, TNFSF13B, VNN1,GADD45B, CD86, PFKFB3 LDLR, CRTAP, PSTPIP2, GADD45B, IL8, 0.82 0.82 0.82TNFRSF6, MAP2K6, IL10, ARG2, LY96, MAPK14, IL18R1 IFNGR1, NCR1,ApolipoproteinCIII, 0.82 0.82 0.82 ANXA3, CSF1R, CCL5, FCGR1A, TIFA,TLR4, INSL3, IL8, ARG2 LY96, Beta2Microglobulin, CCL5, LDLR, 0.82 0.780.86 IRAK4, TIMP1, MKNK1, ApolipoproteinCIII, IL8, SOCS3, ANKRD22, PRV1Protein_MMP9, MAPK14, IL1RN, SOCS3, 0.82 0.77 0.87 MKNK1,ApolipoproteinCIII, IL10, OSM, MAP2K6, TNFSF13B, NCR1, IL18R1 TNFSF13B,FAD104, OSM, TNFRSF6, 0.82 0.81 0.83 TDRD9, TIFA, IL10alpha, INSL3,Protein_MMP9, HLA-DRA, Beta2Microglobulin, ApolipoproteinCIII TLR4,Protein_MMP9, VNN1, IFNGR1, 0.82 0.78 0.85 ITGAM, MCP1, LY96, IRAK2,OSM, TDRD9, IL8, ApolipoproteinCIII MAP2K6, OSM, GADD45B, IL1RN, 0.810.81 0.81 MAPK14, ARG2, LY96, VNN1, TNFRSF6, TGFBI, CD86,Beta2Microglobulin MKNK1, ARG2, CEACAM1, GADD45A, 0.81 0.81 0.81AlphaFetoprotein, GADD45B, HLA-DRA, CReactiveProtein, SOD2, TLR4, LDLR,TNFRSF6 Protein_MMP9, TGFBI, PRV1, 0.81 0.81 0.82 Beta2Microglobulin,TNFSF13B, TLR4, INSL3, Gene_MMP9, ARG2, ApolipoproteinCIII, MKNK1,IL10alpha TLR4, TGFBI, FCGR1A, NCR1, LY96, 0.81 0.81 0.82 IL10, CCL5,IRAK2, INSL3, TDRD9, OSM, BCL2A1 Gene_MMP9, FCGR1A, PSTPIP2, TIFA, 0.810.81 0.82 CSF1R, SOD2, ITGAM, PFKFB3, JAK2, IL8, LY96, OSM CRTAP, MKNK1,TDRD9, LY96, TLR4, 0.81 0.8 0.83 TNFSF10, SOD2, JAK2,Beta2Microglobulin, CD86, PSTPIP2, MAP2K6 TGFBI, TDRD9, ARG2, OSM,TNFSF13B, 0.81 0.78 0.85 CEACAM1, CCL5, CReactiveProtein, TLR4,IL10alpha, LY96, SOCS3 IRAK2, LDLR, ARG2, SOD2, IL10alpha, 0.81 0.780.84 ANKRD22, FCGR1A, Beta2Microglobulin, FAD104, ITGAM, PRV1, OSMFAD104, IL10alpha, INSL3, IL18R1, 0.81 0.83 0.79 IL1RN, MKNK1, MAP2K6,Gene_MMP9, IRAK2, PSTPIP2, CEACAM1, IL6 VNN1, ApolipoproteinCIII, IL10,JAK2, 0.81 0.82 0.8 Protein_MMP9, INSL3, Beta2Microglobulin, OSM, IRAK4,MAP2K6, IL1RN, AlphaFetoprotein BCL2A1, GADD45A, JAK2, FCGR1A, 0.81 0.810.81 FAD104, Gene_MMP9, CRTAP, TDRD9, MAP2K6, CSF1R, PRV1, Protein_MMP9INSL3, TIFA, LY96, FAD104, PSTPIP2, 0.81 0.8 0.82 PFKFB3,Beta2Microglobulin, TIMP1, IL18R1, GADD45A, IL6, AlphaFetoprotein TDRD9,PFKFB3, CSF1R, ITGAM, MCP1, 0.81 0.79 0.83 ARG2, TNFSF13B, PSTPIP2,MAP2K6, ANXA3, OSM, TGFBI TNFSF10, TNFRSF6, Beta2Microglobulin, 0.810.77 0.84 PRV1, SOCS3, IL8, VNN1, TDRD9, CReactiveProtein, GADD45B,TNFSF13B, CD86 MAP2K6, IFNGR1, LY96, 0.81 0.76 0.85 Beta2Microglobulin,GADD45B, ANKRD22, SOCS3, ANXA3, INSL3, Protein_MMP9, CD86, HLA-DRA SOD2,TIMP1, ApolipoproteinCIII, 0.81 0.76 0.85 Protein_MMP9, FAD104, ANXA3,TLR4, CCL5, ITGAM, IRAK4, SOCS3, HLA-DRA ITGAM, INSL3, FCGR1A, ARG2,IRAK2, 0.85 0.82 0.88 FAD104, IRAK4, MAPK14, LY96, TIMP1, PRV1, TLR4,CD86 PRV1, MKNK1, IL8, FAD104, VNN1, 0.85 0.84 0.86 SOCS3, ARG2, MAP2K6,IL1RN, SOD2, IL18R1, NCR1, BCL2A1 NCR1, CEACAM1, IRAK4, ARG2, 0.85 0.790.89 TNFSF13B, PFKFB3, OSM, TNFRSF6, SOCS3, HLA-DRA, TNFSF10, JAK2, SOD2MCP1, Protein_MMP9, IL10alpha, FAD104, 0.84 0.81 0.87 FCGR1A, ITGAM,TGFBI, ApolipoproteinCIII, ARG2, PRV1, CRTAP, TIFA, LDLR ARG2, ANKRD22,GADD45B, IRAK2, 0.84 0.83 0.85 OSM, MKNK1, ANXA3, IL18R1, TNFRSF6,MAP2K6, AlphaFetoprotein, MCP1, ApolipoproteinCIII HLA-DRA, NCR1,CEACAM1, 0.83 0.82 0.85 Beta2Microglobulin, VNN1, AlphaFetoprotein,MCP1, IL6, FCGR1A, OSM, CSF1R, IRAK2, CRTAP CReactiveProtein, SOD2,GADD45A, 0.83 0.86 0.81 ARG2, IRAK4, FCGR1A, IL18R1, TLR4, JAK2, BCL2A1,IL10alpha, TGFBI, AlphaFetoprotein ApolipoproteinCIII, HLA-DRA, TNFSF10,0.83 0.83 0.83 TLR4, IL10, GADD45B, BCL2A1, IL6, CCL5, INSL3, MAP2K6,LDLR, IFNGR1 TIFA, JAK2, HLA-DRA, SOCS3, ARG2, 0.83 0.79 0.87 OSM,AlphaFetoprotein, MAPK14, IRAK2, IFNGR1, FCGR1A, MAP2K6, PRV1Beta2Microglobulin, IRAK2, MKNK1, 0.83 0.83 0.83 ANKRD22, CD86, OSM,CSF1R, TNFSF10, IFNGR1, TLR4, MCP1, FAD104, TGFBI VNN1, FCGR1A, ANKRD22,CRTAP, 0.83 0.82 0.84 ANXA3, IL8, PFKFB3, NCR1, TLR4, AlphaFetoprotein,TIFA, IRAK4, CD86 Gene_MMP9, INSL3, FCGR1A, LDLR, 0.83 0.81 0.84 OSM,PFKFB3, ANKRD22, IL1RN, IL8, IFNGR1, TDRD9, BCL2A1, TNFSF13B PFKFB3,AlphaFetoprotein, IRAK4, NCR1, 0.82 0.81 0.84 TNFSF10, TDRD9, JAK2,FAD104, IL10alpha, PRV1, CReactiveProtein, TGFBI, Protein_MMP9Gene_MMP9, MAP2K6, MAPK14, 0.82 0.76 0.88 CReactiveProtein, PFKFB3,CCL5, CSF1R, INSL3, MKNK1, ARG2, FAD104, SOD2, Protein_MMP9 IL8,TNFSF13B, ARG2, TIFA, CRTAP, 0.82 0.85 0.79 OSM, IL18R1, MCP1, IRAK4,LY96, AlphaFetoprotein, TDRD9, CReactiveProtein PSTPIP2, CEACAM1,GADD45B, 0.82 0.83 0.81 MAPK14, ARG2, FCGR1A, ITGAM, TGFBI, IL10alpha,OSM, PRV1, IL8, TLR4 OSM, CReactiveProtein, CD86, LY96, 0.82 0.82 0.82IL10alpha, FAD104, TDRD9, IL6, ApolipoproteinCIII, LDLR, CSF1R, IL18R1,MCP1 JAK2, IL8, ARG2, OSM, BCL2A1, TIFA, 0.82 0.83 0.8 IL6, Gene_MMP9,PRV1, TLR4, IL1RN, LY96, IRAK2 IL6, INSL3, BCL2A1, TLR4, HLA-DRA, 0.820.81 0.82 IL10alpha, MKNK1, TDRD9, GADD45A, OSM, SOCS3, CCL5, MAPK14LDLR, FCGR1A, SOD2, LY96, MKNK1, 0.82 0.77 0.86 PRV1, MAP2K6, NCR1,Protein_MMP9, SOCS3, AlphaFetoprotein, IFNGR1, INSL3 TNFRSF6, ARG2,INSL3, ANXA3, IL10, 0.82 0.77 0.87 TIFA, ITGAM, VNN1, SOD2, TIMP1,CSF1R, Protein_MMP9, SOCS3 IL10alpha, TNFRSF6, ARG2, TIMP1, IL8, 0.810.85 0.78 CSF1R, MAP2K6, IRAK4, PFKFB3, FCGR1A, AlphaFetoprotein, OSM,HLA- DRA Protein_MMP9, CD86, IFNGR1, TIMP1, 0.81 0.81 0.81 IL1RN,FCGR1A, ARG2, TIFA, IL8, CRTAP, CSF1R, IL6, ITGAM CEACAM1, ANKRD22,CCL5, TLR4, 0.81 0.81 0.82 IRAK4, Beta2Microglobulin, MAP2K6, PRV1,TGFBI, FAD104, SOD2, JAK2, MCP1 CD86, VNN1, PSTPIP2, PFKFB3, 0.81 0.860.77 CReactiveProtein, IL6, TLR4, CCL5, FCGR1A, TDRD9, TNFRSF6, CSF1R,CRTAP LDLR, OSM, MCP1, CD86, IL1RN, 0.81 0.83 0.78 Protein_MMP9, MAP2K6,FCGR1A, IL8, CEACAM1, PFKFB3, IRAK4, LY96 CReactiveProtein, TNFSF13B,0.81 0.79 0.82 ApolipoproteinCIII, IRAK2, VNN1, FCGR1A, PFKFB3, HLA-DRA,ANKRD22, SOD2, CD86, TGFBI, Beta2Microglobulin LY96, TNFSF10, PRV1,PSTPIP2, SOCS3, 0.81 0.79 0.83 TIMP1, IFNGR1, ARG2, CEACAM1, CCL5,TNFSF13B, LDLR, ApolipoproteinCIII PRV1, JAK2, FCGR1A, VNN1, SOCS3, 0.810.79 0.83 TIFA, CRTAP, INSL3, IFNGR1, TDRD9, CEACAM1, Protein_MMP9, IL8GADD45A, SOCS3, OSM, CD86, ITGAM, 0.81 0.77 0.84 ApolipoproteinCIII,FAD104, INSL3, PSTPIP2, IL18R1, AlphaFetoprotein, TDRD9, MAP2K6 PSTPIP2,VNN1, IL1RN, CSF1R, CD86, 0.81 0.77 0.85 TLR4, IRAK4, IFNGR1, CRTAP,TNFSF10, SOD2, TIFA, TDRD9 TNFRSF6, IFNGR1, TNFSF13B, MAP2K6, 0.85 0.880.83 MKNK1, ANXA3, TGFBI, OSM, ARG2, Beta2Microglobulin,CReactiveProtein, LY96, ApolipoproteinCIII, TIFA CSF1R, TLR4, IL6,TNFSF13B, 0.84 0.85 0.83 Beta2Microglobulin, IRAK4, FCGR1A, CCL5, ITGAM,VNN1, TIFA, CRTAP, PFKFB3, TDRD9 CReactiveProtein, IL6, MAP2K6, OSM,0.84 0.82 0.85 ARG2, ANKRD22, JAK2, HLA-DRA, ApolipoproteinCIII, MAPK14,TLR4, TNFSF13B, IFNGR1, IL10alpha VNN1, GADD45B, IRAK2, TGFBI, NCR1,0.84 0.77 0.89 IL6, CEACAM1, CRTAP, Gene_MMP9, TNFRSF6, CD86, TDRD9,CReactiveProtein, IL10 CRTAP, IL18R1, Beta2Microglobulin, 0.83 0.81 0.85ANXA3, TDRD9, MKNK1, Protein_MMP9, IL6, TNFSF10, OSM, MCP1, PFKFB3,ApolipoproteinCIII, VNN1 PSTPIP2, IL8, IL18R1, CEACAM1, HLA- 0.83 0.790.86 DRA, OSM, NCR1, MCP1, FCGR1A, TNFRSF6, TLR4, IRAK2, Protein_MMP9,CReactiveProtein PRV1, IRAK4, FAD104, TGFBI, 0.83 0.83 0.82Protein_MMP9, INSL3, AlphaFetoprotein, CD86, VNN1, CSF1R,Beta2Microglobulin, GADD45B, BCL2A1, IL10 CD86, MAP2K6, PSTPIP2,TNFSF10, 0.83 0.81 0.84 OSM, GADD45B, TLR4, HLA-DRA, LY96, TNFSF13B,ARG2, SOD2, PRV1, Beta2Microglobulin TIFA, CSF1R, IL10alpha, IFNGR1,0.83 0.77 0.87 CEACAM1, CRTAP, ANKRD22, FCGR1A, MAP2K6, FAD104, PSTPIP2,MAPK14, ARG2, IRAK2 CReactiveProtein, TDRD9, IL8, ITGAM, 0.82 0.86 0.78IL10alpha, TNFRSF6, SOD2, MCP1, SOCS3, MKNK1, FAD104, MAP2K6, IFNGR1,AlphaFetoprotein TLR4, ANKRD22, IL10alpha, 0.82 0.86 0.78CReactiveProtein, ApolipoproteinCIII, BCL2A1, FCGR1A, SOD2, OSM, IFNGR1,TGFBI, TIFA, VNN1, CEACAM1 FCGR1A, IRAK4, MAP2K6, ANXA3, 0.82 0.86 0.79MAPK14, INSL3, AlphaFetoprotein, IL8, MKNK1, ARG2, VNN1, TIMP1, CSF1R,GADD45A ANKRD22, HLA-DRA, IFNGR1, 0.82 0.81 0.84 GADD45A, TNFSF13B,FAD104, LDLR, IL10alpha, IL6, MAPK14, ApolipoproteinCIII, PRV1,CReactiveProtein, TIMP1 IL10, PSTPIP2, INSL3, LY96, NCR1, 0.82 0.76 0.87MAPK14, VNN1, MCP1, PRV1, ApolipoproteinCIII, TIMP1, Protein_MMP9,TDRD9, PFKFB3 TNFRSF6, TGFBI, LY96, TDRD9, CRTAP, 0.82 0.85 0.79AlphaFetoprotein, TNFSF10, CCL5, JAK2, IL6, IRAK2, HLA-DRA, OSM,ApolipoproteinCIII TIFA, Gene_MMP9, IL18R1, TDRD9, 0.82 0.84 0.8 SOCS3,TIMP1, IL6, CCL5, ARG2, CSF1R, OSM, IL10alpha, IL8, TNFSF13B PSTPIP2,PRV1, MAPK14, OSM, CRTAP, 0.82 0.82 0.82 IFNGR1, IL6, FAD104, IL18R1,JAK2, GADD45B, LY96, BCL2A1, TLR4 GADD45A, IL6, TGFBI, BCL2A1, CRTAP,0.82 0.8 0.84 CCL5, TIFA, TLR4, CD86, PRV1, FAD104, TDRD9, TNFSF10,SOCS3 Beta2Microglobulin, JAK2, TDRD9, 0.82 0.79 0.85 PSTPIP2, HLA-DRA,IL1RN, TGFBI, INSL3, ARG2, LDLR, AlphaFetoprotein, IRAK2, SOD2, MAPK14CD86, FAD104, AlphaFetoprotein, 0.82 0.77 0.86 Gene_MMP9, MCP1, HLA-DRA,INSL3, PSTPIP2, IL1RN, ITGAM, TIMP1, Protein_MMP9, IL6, IRAK4 ANKRD22,MAPK14, GADD45A, TDRD9, 0.82 0.88 0.75 IL10alpha, Protein_MMP9, ARG2,CD86, TIMP1, IRAK2, TIFA, VNN1, OSM, ITGAM SOCS3, IL1RN, CEACAM1,FCGR1A, 0.82 0.85 0.79 LDLR, CCL5, CReactiveProtein, AlphaFetoprotein,ARG2, IL6, CD86, MCP1, INSL3, IL18R1 ANKRD22, FAD104,ApolipoproteinCIII, 0.82 0.84 0.79 IRAK2, TNFSF13B, TGFBI, TLR4, CRTAP,MCP1, LDLR, JAK2, SOD2, PSTPIP2, Protein_MMP9 BCL2A1, IL1RN, FCGR1A,GADD45A, 0.82 0.84 0.79 JAK2, NCR1, TDRD9, TIFA, TNFSF10, Protein_MMP9,CRTAP, CSF1R, IL6, INSL3 SOD2, ITGAM, ApolipoproteinCIII, 0.82 0.8 0.83ANXA3, FAD104, IL6, ARG2, CD86, TGFBI, SOCS3, OSM, TDRD9, IL18R1, LY96VNN1, IRAK2, ApolipoproteinCIII, IL10, 0.82 0.79 0.84 TDRD9, FCGR1A,IL8, TIMP1, MCP1, JAK2, TIFA, TGFBI, OSM, MAPK14 ApolipoproteinCIII,IL10, TDRD9, ARG2, 0.82 0.78 0.85 IRAK4, ANXA3, TNFRSF6,CReactiveProtein, INSL3, JAK2, IL1RN, IL6, NCR1, Gene_MMP9 CD86, CSF1R,TNFSF13B, FCGR1A, 0.81 0.83 0.79 MCP1, GADD45A, LDLR, IRAK2, CCL5,Beta2Microglobulin, SOCS3, MAP2K6, LY96, INSL3 MCP1, NCR1, TGFBI, TDRD9,MAP2K6, 0.81 0.83 0.8 ApolipoproteinCIII, INSL3, LY96, IFNGR1, JAK2,Protein_MMP9, GADD45B, IRAK4, CCL5 Beta2Microglobulin, FCGR1A, TNFSF13B,0.81 0.82 0.8 OSM, IRAK4, IRAK2, IL8, MAPK14, PSTPIP2, TIFA, TIMP1,ApolipoproteinCIII, MAP2K6, TLR4 TNFSF13B, LY96, OSM, MAP2K6, IRAK2,0.81 0.82 0.81 CRTAP, JAK2, PFKFB3, BCL2A1, CReactiveProtein, INSL3,GADD45A, TIFA, IL10alpha OSM, JAK2, GADD45A, CEACAM1, 0.81 0.82 0.81ARG2, NCR1, TLR4, PRV1, PFKFB3, IL8, Beta2Microglobulin, GADD45B, HLA-DRA, INSL3 LY96, TIFA, CSF1R, IL10, SOCS3, ARG2, 0.81 0.79 0.83 IRAK4,CD86, IL10alpha, Protein_MMP9, TNFSF10, ITGAM, Gene_MMP9, LDLRAlphaFetoprotein, ApolipoproteinCIII, 0.81 0.78 0.84 SOD2, PSTPIP2,CSF1R, Beta2Microglobulin, NCR1, GADD45B, FCGR1A, CReactiveProtein,CEACAM1, CD86, Protein_MMP9, HLA-DRA MAPK14, ARG2, TNFSF10, TNFSF13B,0.81 0.74 0.88 FAD104, ANKRD22, GADD45A, ANXA3, CReactiveProtein, NCR1,IFNGR1, OSM, Protein_MMP9, IL18R1 VNN1, NCR1, IL10alpha, ARG2, IL6, 0.810.84 0.79 LY96, CReactiveProtein, JAK2, TGFBI, SOCS3, CRTAP, ITGAM,IRAK4, PRV1 TNFSF13B, CReactiveProtein, INSL3, 0.81 0.78 0.84 CEACAM1,Beta2Microglobulin, CD86, IL6, JAK2, ApolipoproteinCIII, IL18R1, ANXA3,PSTPIP2, SOD2, IL1RN IRAK2, FCGR1A, Gene_MMP9, BCL2A1, 0.81 0.78 0.84TGFBI, PSTPIP2, CEACAM1, GADD45A, CCL5, TNFSF13B, ARG2, IL8, TIFA,IL18R1

In some embodiments, the methods or kits respectively described orreferenced in Section 5.2 and Section 5.3 use any one of the subsets ofbiomarkers listed in Table O. The subsets of biomarkers listed in TableO were identified in the computational experiments described in Section6.14.5, below, in which 4600 random subcombinations of the biomarkerslisted in Table I were tested. Table O lists some of the biomarker setsthat provided high accuracy scores against the validation populationdescribed in Section 6.14.5. Each row of Table O lists a single set ofbiomarkers that can be used in the methods and kits respectivelyreferenced in Sections 5.2 and 5.3. In other words, each row of Table Odescribes a set of biomarkers that can be used to discriminate betweensepsis and SIRS subjects. In some embodiments, nucleic acid forms of thebiomarkers listed in Table O are used in the methods and kitsrespectively referenced in Sections 5.2 and 5.3. In some embodiments,protein forms of the biomarkers listed in Table O are used. In someembodiments, some of the biomarkers in a biomarker set from Table O arein protein form and some of the biomarkers in the same biomarker setfrom Table O are in nucleic acid form in the methods and kitsrespectively referenced in Sections 5.2 and 5.3.

In some embodiments, a given set of biomarkers from Table O is used withthe addition of one, two, three, four, five, six, seven, eight, or nineor more additional biomarkers from from any one of Table 30, 31, 32, 33,34, or 36 that are not within the given set of biomarkers from Table O.In Table O, accuracy, specificity, and senstitivity are described withreference to T⁻³⁶ time point data described in Section 6.14.6, below.

TABLE O Exemplary sets of biomarkers used in the methods or kitsreferenced in Sections 5.2 and 5.3 BIOMARKER SET ACCURACY SPECIFICITYSENSISTIVITY SOCS3, ApolipoproteinCIII, NCR1 0.81 0.75 0.85 IL8, PRV1,CEACAM1 0.8 0.79 0.8 PSTPIP2, TLR4, GADD45B 0.8 0.72 0.87 ARG2, PRV1,MKNK1 0.79 0.71 0.85 CD86, SOCS3, TLR4 0.79 0.74 0.82 PRV1, GADD45B,TNFSF13B, ITGAM 0.83 0.73 0.91 PRV1, ApolipoproteinCIII, FCGR1A, 0.810.78 0.84 LDLR TNFRSF6, MAP2K6, PRV1, ANKRD22 0.81 0.77 0.85 PRV1, ARG2,CD86, CEACAM1 0.81 0.8 0.82 GADD45B, CReactiveProtein, PRV1, CD86 0.810.73 0.88 GADD45B, TNFSF13B, FAD104, PFKFB3 0.81 0.73 0.86 PRV1, FAD104,IL18R1, MCP1 0.8 0.69 0.88 PRV1, IRAK2, PSTPIP2, ANXA3 0.8 0.68 0.87FCGR1A, JAK2, MKNK1, PRV1 0.8 0.65 0.91 IL10, TNFSF13B, GADD45B, CEACAM10.79 0.73 0.85 Beta2Microglobulin, GADD45B, ARG2, 0.81 0.73 0.88TNFSF13B, OSM CD86, BCL2A1, PSTPIP2, PRV1, JAK2 0.8 0.71 0.89 GADD45A,GADD45B, CSF1R, MAP2K6, 0.8 0.69 0.88 PSTPIP2 AlphaFetoprotein,CReactiveProtein, 0.8 0.76 0.82 GADD45B, MAPK14, ANXA3 PRV1, FCGR1A,NCR1, CReactiveProtein, 0.8 0.74 0.84 TNFRSF6 MAPK14, CSF1R, OSM, IL1RN,TLR4 0.8 0.74 0.84 IRAK4, MAPK14, GADD45B, TNFSF13B, 0.8 0.71 0.86 CSF1RITGAM, ANXA3, Beta2Microglobulin, 0.79 0.76 0.82 PRV1, IRAK2 NCR1, MCP1,PRV1, CD86, FCGR1A 0.79 0.72 0.86 CRTAP, Beta2Microglobulin, TDRD9, 0.790.65 0.91 GADD45A, PRV1 PRV1, PFKFB3, FCGR1A, TIFA, 0.79 0.73 0.84ANKRD22 PRV1, ApolipoproteinCIII, FCGR1A, 0.79 0.72 0.85 Protein_MMP9,TIMP1 FCGR1A, IRAK2, TNFSF13B, OSM, 0.84 0.79 0.89 CRTAP, PFKFB3 ANXA3,CEACAM1, PRV1, OSM, MCP1, 0.81 0.77 0.84 CCL5 IRAK4, TNFSF10, MCP1,PRV1, MKNK1, 0.81 0.75 0.84 SOCS3 TGFBI, CEACAM1, CD86, MAPK14, 0.8 0.760.83 LDLR, PRV1 MCP1, GADD45B, CEACAM1, TIMP1, 0.8 0.76 0.83 MAP2K6,IFNGR1 LY96, PRV1, MCP1, IRAK2, CD86, 0.8 0.76 0.83 TNFSF10 BCL2A1,PRV1, LDLR, TNFSF10, 0.8 0.73 0.85 IL18R1, SOCS3 SOCS3,ApolipoproteinCIII, FCGR1A, 0.79 0.7 0.87 TNFSF13B, IFNGR1,Beta2Microglobulin ARG2, PSTPIP2, TNFRSF6, GADD45B, 0.79 0.82 0.77MAPK14, TIMP1 NCR1, IL8, FCGR1A, IL1RN, 0.79 0.73 0.84ApolipoproteinCIII, IFNGR1 LDLR, MAP2K6, PRV1, TIMP1, HLA- 0.79 0.720.85 DRA, CCL5 TIFA, GADD45B, HLA-DRA, CEACAM1, 0.79 0.74 0.83 OSM, ARG2TIMP1, GADD45A, MKNK1, SOCS3, 0.79 0.73 0.83 LDLR, TNFSF10 SOD2, LY96,PRV1, FAD104, BCL2A1, 0.79 0.72 0.83 GADD45A CEACAM1, BCL2A1, IRAK4,LDLR, 0.79 0.69 0.85 TIFA, IL10alpha TNFSF10, TIFA, GADD45B, ANXA3, 0.780.65 0.88 BCL2A1, TNFRSF6 Beta2Microglobulin, TIMP1, GADD45A, 0.78 0.790.77 CRTAP, FAD104, GADD45B ApolipoproteinCIII, IL18R1, CSF1R, 0.78 0.720.83 LDLR, FCGR1A, MCP1 MKNK1, GADD45B, IL1RN, NCR1, IL10, 0.78 0.710.83 LDLR CD86, IL10, IFNGR1, SOCS3, TDRD9, 0.78 0.7 0.85 MCP1 PRV1,SOD2, INSL3, TIFA, IRAK2, MCP1 0.78 0.7 0.84 AlphaFetoprotein,Protein_MMP9, 0.83 0.79 0.87 ANKRD22, HLA-DRA, MAP2K6, GADD45B, CEACAM1TNFSF13B, OSM, PRV1, CSF1R, IFNGR1, 0.83 0.79 0.85 TNFRSF6, FCGR1AFCGR1A, CCL5, TNFSF13B, 0.81 0.83 0.8 Gene_MMP9, IL6, MAP2K6, OSMGADD45B, IL1RN, Beta2Microglobulin, 0.81 0.68 0.91 VNN1, PRV1, CD86,IL10 IL8, TIFA, IL18R1, SOD2, CSF1R, 0.8 0.81 0.8 FAD104, PRV1 MAP2K6,SOD2, IL18R1, LDLR, ANXA3, 0.8 0.78 0.82 CD86, GADD45B ANKRD22, PRV1,TIMP1, NCR1, 0.8 0.75 0.84 GADD45A, FCGR1A, TNFSF13B IL10alpha, CRTAP,IL10, TIMP1, TIFA, 0.8 0.73 0.87 PRV1, ARG2 TNFRSF6, TLR4, LY96, CSF1R,0.8 0.7 0.88 GADD45B, CCL5, INSL3 TDRD9, ANXA3, TNFSF10, TNFRSF6, 0.80.68 0.9 PRV1, CCL5, IFNGR1 CD86, GADD45B, CReactiveProtein, 0.8 0.820.78 LDLR, CCL5, FAD104, IL8 IRAK4, TGFBI, PRV1, CEACAM1, 0.8 0.75 0.83IFNGR1, PSTPIP2, TLR4 OSM, Gene_MMP9, TLR4, TDRD9, CCL5, 0.8 0.72 0.84CRTAP, HLA-DRA CRTAP, CEACAM1, FAD104, GADD45A, 0.79 0.72 0.84 PRV1,MAP2K6, TNFSF10 TNFRSF6, MKNK1, SOD2, TGFBI, MCP1, 0.79 0.72 0.86GADD45B, ANKRD22 TIMP1, BCL2A1, TNFSF10, PRV1, HLA- 0.79 0.7 0.86 DRA,CRTAP, PFKFB3 INSL3, ANXA3, Beta2Microglobulin, 0.79 0.7 0.86 GADD45B,TNFRSF6, ANKRD22, LDLR TIFA, GADD45B, HLA-DRA, CD86, IL10, 0.79 0.720.83 IL10alpha, MCP1 FCGR1A, CReactiveProtein, BCL2A1, 0.79 0.71 0.83GADD45B, PRV1, PFKFB3, MAP2K6 IL8, INSL3, ANKRD22, TNFSF10, HLA- 0.790.7 0.85 DRA, PFKFB3, CSF1R IL10alpha, MCP1, SOD2, TNFSF13B, 0.78 0.750.81 CRTAP, MAP2K6, PRV1 FAD104, SOD2, LY96, IL8, IRAK4, PRV1, 0.78 0.730.83 Protein_MMP9 MAPK14, OSM, PRV1, CRTAP, 0.78 0.7 0.85 IL10alpha,MKNK1, IFNGR1 OSM, AlphaFetoprotein, IFNGR1, SOD2, 0.78 0.73 0.82GADD45A, CEACAM1, MKNK1 IL18R1, TDRD9, INSL3, JAK2, 0.78 0.7 0.85Protein_MMP9, TNFRSF6, NCR1 IFNGR1, CEACAM1, JAK2, SOD2, HLA- 0.83 0.820.84 DRA, MAPK14, PRV1, VNN1 NCR1, IRAK2, MAP2K6, 0.83 0.83 0.82CReactiveProtein, FCGR1A, ARG2, CD86, SOCS3 GADD45B, ARG2, GADD45A,IL10alpha, 0.81 0.75 0.84 TDRD9, PFKFB3, CReactiveProtein, OSM PRV1,ITGAM, IL1RN, MAPK14, 0.81 0.74 0.85 TNFSF10, SOD2, ARG2, PFKFB3TNFRSF6, Beta2Microglobulin, PSTPIP2, 0.81 0.73 0.87 IL8, SOCS3,GADD45B, CRTAP, IFNGR1 CReactiveProtein, LY96, MAP2K6, 0.8 0.78 0.82IL18R1, INSL3, OSM, CSF1R, IL6 ITGAM, PRV1, MAP2K6, IL8, OSM, 0.8 0.740.86 SOD2, IRAK4, CCL5 CReactiveProtein, OSM, PSTPIP2, 0.8 0.73 0.85TNFSF10, ANKRD22, TDRD9, INSL3, CD86 ANKRD22, CD86, PRV1, ANXA3, IL10,0.79 0.81 0.78 TNFSF13B, TIFA, AlphaFetoprotein ApolipoproteinCIII,MKNK1, FCGR1A, 0.79 0.75 0.82 PSTPIP2, VNN1, TNFRSF6, AlphaFetoprotein,OSM PRV1, CCL5, PFKFB3, TNFSF13B, 0.79 0.74 0.83 TIMP1, LDLR, ANKRD22,MAP2K6 ARG2, VNN1, ANKRD22, IFNGR1, 0.79 0.74 0.85 IL1RN, CD86, FAD104,GADD45B IL10, PFKFB3, NCR1, TNFSF13B, MCP1, 0.79 0.7 0.86 MAPK14, PRV1,TIMP1 ApolipoproteinCIII, INSL3, IL10alpha, 0.79 0.74 0.83 FCGR1A,IL1RN, IL6, TNFRSF6, IL8 IL10, FAD104, CCL5, SOCS3, CD86, 0.79 0.79 0.78HLA-DRA, LDLR, GADD45A PFKFB3, CReactiveProtein, MAPK14, 0.79 0.74 0.81TNFSF10, BCL2A1, ITGAM, IL10alpha, TDRD9 Beta2Microglobulin, TNFSF13B,0.78 0.73 0.82 ANKRD22, MCP1, TDRD9, IRAK4, TIMP1, OSM PSTPIP2, MAP2K6,AlphaFetoprotein, 0.78 0.69 0.84 TDRD9, PFKFB3, IL8, ANXA3, PRV1 TIFA,AlphaFetoprotein, PRV1, IL18R1, 0.78 0.68 0.87 Gene_MMP9, VNN1, TDRD9,TNFRSF6 IRAK2, FAD104, PRV1, GADD45A, TIFA, 0.78 0.72 0.83 MCP1, TIMP1,SOD2 IL6, CSF1R, MAP2K6, ANXA3, MCP1, 0.78 0.72 0.82 PRV1, ITGAM,AlphaFetoprotein CCL5, IL10alpha, GADD45B, LDLR, 0.78 0.69 0.84 PSTPIP2,CD86, HLA-DRA, TLR4 LDLR, CRTAP, NCR1, TNFRSF6, 0.78 0.69 0.84ApolipoproteinCIII, MAPK14, FCGR1A, IRAK2 TGFBI, ANXA3, IL18R1, MAP2K6,0.78 0.67 0.87 FCGR1A, IL10, OSM, PRV1 NCR1, JAK2, ANKRD22, IL1RN,ANXA3, 0.82 0.78 0.86 LDLR, CD86, IFNGR1, OSM CSF1R, TDRD9, FAD104,TNFSF10, 0.82 0.79 0.84 OSM, LDLR, MAPK14, TIFA, BCL2A1 TNFSF10, IFNGR1,TNFRSF6, GADD45B, 0.82 0.75 0.87 CCL5, TNFSF13B, ANXA3, JAK2, PRV1TNFSF13B, CD86, TIFA, SOCS3, 0.81 0.68 0.91 GADD45B, ARG2, TNFSF10,IRAK4, IL10 FCGR1A, PSTPIP2, CEACAM1, IL1RN, 0.81 0.79 0.83 FAD104, IL6,INSL3, CSF1R, PRV1 IL1RN, SOD2, TGFBI, ApolipoproteinCIII, 0.8 0.83 0.78JAK2, CEACAM1, IRAK2, IFNGR1, OSM TDRD9, CD86, Protein_MMP9, TNFRSF6,0.8 0.78 0.82 SOCS3, MCP1, AlphaFetoprotein, TIFA, INSL3 BCL2A1, TGFBI,TLR4, IL8, LDLR, 0.8 0.77 0.83 ANKRD22, TNFSF13B, IL10, GADD45BTNFSF13B, AlphaFetoprotein, TDRD9, 0.8 0.73 0.86 MAPK14, SOCS3, ANXA3,IL1RN, CRTAP, TNFRSF6 IL6, TNFRSF6, MCP1, JAK2, GADD45A, 0.8 0.75 0.84TIFA, ARG2, FCGR1A, ANKRD22 PSTPIP2, ANXA3, MCP1, FAD104, PRV1, 0.8 0.730.86 ANKRD22, NCR1, HLA-DRA, FCGR1A IL8, PRV1, TDRD9,Beta2Microglobulin, 0.8 0.73 0.86 IL10alpha, VNN1, INSL3, TIFA, CSF1RGADD45B, TNFRSF6, OSM, IRAK4, 0.8 0.71 0.88 AlphaFetoprotein, IL1RN,TNFSF13B, MCP1, FAD104 ANKRD22, OSM, INSL3, IFNGR1, 0.8 0.69 0.89 MKNK1,GADD45B, TDRD9, MAP2K6, IRAK4 NCR1, JAK2, ANKRD22, IL1RN, ANXA3, 0.820.78 0.86 LDLR, CD86, IFNGR1, OSM ApolipoproteinCIII, ANXA3, IL18R1,0.84 0.73 0.93 PRV1, CD86, LDLR, TDRD9, CReactiveProtein, MAP2K6, CSF1R,CRTAP CCL5, Protein_MMP9, NCR1, PRV1, 0.82 0.74 0.88 TNFRSF6, TGFBI,HLA-DRA, FCGR1A, IFNGR1, CSF1R, MCP1 GADD45B, CSF1R, IL1RN, PSTPIP2,0.81 0.81 0.81 PRV1, ApolipoproteinCIII, ARG2, SOCS3, FAD104, ITGAM,TIMP1 JAK2, MKNK1, CRTAP, GADD45B, 0.81 0.79 0.83 OSM, INSL3, TIMP1,TIFA, TNFRSF6, AlphaFetoprotein, CD86 ApolipoproteinCIII, CD86, FCGR1A,0.81 0.73 0.88 ARG2, GADD45B, IL8, CRTAP, IFNGR1, TIMP1, ANXA3, HLA-DRAMCP1, IL8, TNFSF13B, AlphaFetoprotein, 0.81 0.78 0.83 LDLR,Protein_MMP9, JAK2, FAD104, IRAK2, TNFRSF6, GADD45B TLR4, NCR1, CCL5,IL6, 0.81 0.76 0.85 CReactiveProtein, IRAK4, AlphaFetoprotein, FCGR1A,ApolipoproteinCIII, GADD45B, PRV1 ANKRD22, OSM, VNN1, LDLR, 0.8 0.81 0.8ApolipoproteinCIII, IL1RN, SOCS3, MAPK14, GADD45B, JAK2, ITGAM NCR1,ARG2, GADD45B, GADD45A, 0.8 0.76 0.84 CD86, TNFSF10, CCL5, PSTPIP2,Beta2Microglobulin, CRTAP, LDLR SOCS3, JAK2, IL1RN, IFNGR1, CRTAP, 0.80.76 0.84 TIMP1, Protein_MMP9, VNN1, TNFRSF6, CD86, ANKRD22 OSM,PSTPIP2, IL1RN, AlphaFetoprotein, 0.8 0.86 0.75 PRV1, IL6, LY96, IL18R1,CSF1R, TNFSF13B, LDLR IL10alpha, CReactiveProtein, TIFA, NCR1, 0.8 0.750.84 CRTAP, TGFBI, PFKFB3, LDLR, IRAK4, GADD45B, TDRD9ApolipoproteinCIII, ANXA3, IL18R1, 0.84 0.73 0.93 PRV1, CD86, LDLR,TDRD9, CReactiveProtein, MAP2K6, CSF1R, CRTAP

In some embodiments, the methods or kits respectively described orreferenced in Section 5.2 and Section 5.3 use at least two differentbiomarkers that each contain a probeset listed in any one of FIG. 6, 14,17, or 26. In a particular embodiment, a biomarker profile comprises atleast two different biomarkers that each contain one of the probesetslisted in any one of FIG. 6, 14, 17, or 26, biomarkers that each containthe complement of one of the probesets of any one of FIG. 6, 14, 17, or26, or biomarkers that each contain an amino acid sequence encoded by agene that either contains one of the probesets of any one of FIG. 6, 14,17, or 26, or the complement of one of the probesets of any one of FIG.6, 14, 17, or 26. Such biomarkers can be, for example, mRNA transcripts,cDNA or some other nucleic acid, for example, amplified nucleic acid, orproteins. The biomarker profile further comprises a respectivecorresponding feature for the at least two biomarkers. Generally, the atleast two biomarkers are derived from at least two different genes. Inthe case where a biomarker is based upon a gene that includes thesequence of a probeset listed in any one of FIG. 6, 14, 17, or 26, thebiomarker can be, for example, a transcript made by the gene, acomplement thereof, or a discriminating fragment or complement thereof,or a cDNA thereof, or a discriminating fragment of the cDNA, or adiscriminating amplified nucleic acid molecule corresponding to all or aportion of the transcript or its complement, or a protein encoded by thegene, or a discriminating fragment of the protein, or an indication ofany of the above. Further still, the biomarker can be, for example, aprotein encoded by a gene that includes a probeset sequence described inany one of FIG. 6, 14, 17, or 26, or a discriminating fragment of theprotein, or an indication of any of the above. Here, a discriminatingmolecule or fragment is a molecule or fragment that, when detected,indicates presence or abundance of the above-identified transcript,cDNA, amplified nucleic acid, or protein. In some embodiments, thebiomarker profile comprises at least 2, 3, 4, 5, 6, 7, 8, 9, or 10different biomarkers that each contains a probeset listed in any one ofFIG. 6, 14, 17, or 26.

In some embodiments, the methods or kits respectively described orreferenced in Section 5.2 and Section 5.3 use at least two differentbiomarkers listed in any one of FIG. 39, 43, 52, 53, or 56. In aparticular embodiment, the biomarker profile comprises at least twodifferent biomarkers listed in any one of FIG. 39, 43, 52, 53, or 56.The biomarker profile further comprises a respective correspondingfeature for the at least two biomarkers. Generally, the at least twobiomarkers are derived from at least two different genes. In the casewhere a biomarker in the at least two different biomarkers is listed inany one of FIG. 39, 43, 52, 53, or 56, the biomarker can be, forexample, a transcript made by the listed gene, a complement thereof, ora discriminating fragment or complement thereof, or a cDNA thereof, or adiscriminating fragment of the cDNA, or a discriminating amplifiednucleic acid molecule corresponding to all or a portion of thetranscript or its complement, or a protein encoded by the gene, or adiscriminating fragment of the protein, or an indication of any of theabove. Further still, the biomarker can be, for example, a proteinencoded by a gene listed in any one of FIG. 39, 43, 52, 53, or 56, or adiscriminating fragment of the protein, or an indication of any of theabove. Here, a discriminating molecule or fragment is a molecule orfragment that, when detected, indicates presence or abundance of theabove-identified transcript, cDNA, amplified nucleic acid, or protein.In accordance with this embodiment, the biomarker profiles of thepresent invention can be obtained using any standard assay known tothose skilled in the art, or in an assay described herein, to detect abiomarker. Such assays are capable, for example, of detecting theproducts of expression (e.g., nucleic acids and/or proteins) of aparticular gene or allele of a gene of interest (e.g., a gene disclosedin Table 30). In one embodiment, such an assay utilizes a nucleic acidmicroarray. In some embodiments, the biomarker profile comprises atleast two different biomarkers from any one of FIG. 39, 43, 52, 53, or56. In some embodiments, the biomarker profile comprises at least 2, 3,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20different biomarkers from any one of FIG. 39, 43, 52, 53, or 56.

In some embodiments, the methods or kits respectively described orreferenced in Section 5.2 and Section 5.3 use specific biomarkerscontaining probes from any one of the probeset collections listed inTable P. In a particular embodiment, a biomarker profile comprises atleast two different biomarkers that each contain one of the probesetslisted in any one of the probeset collections of Table P, biomarkersthat each contain the complement of one of the probesets from any one ofthe probeset collections of Table P, or biomarkers that each contain anamino acid sequence encoded by a gene that either contains one of theprobesets from any one of the probeset collections of Table P, or thecomplement of one of the probesets of any one of the probesetcollections of Table P. Such biomarkers can be, for example, mRNAtranscripts, cDNA or some other nucleic acid, for example, amplifiednucleic acid, or proteins. The biomarker profile further comprises arespective corresponding feature for the at least two biomarkers.Generally, the at least two biomarkers are derived from at least twodifferent genes. In the case where a biomarker is based upon a gene thatincludes the sequence of a probeset listed in any one of the probesetcollections of Table P, the biomarker can be, for example, a transcriptmade by the gene, a complement thereof, or a discriminating fragment orcomplement thereof, or a cDNA thereof, or a discriminating fragment ofthe cDNA, or a discriminating amplified nucleic acid moleculecorresponding to all or a portion of the transcript or its complement,or a protein encoded by the gene, or a discriminating fragment of theprotein, or an indication of any of the above. Further still, thebiomarker can be, for example, a protein encoded by a gene that includesa probeset sequence from any one of the probeset collections listed inTable P, or a discriminating fragment of the protein, or an indicationof any of the above. Here, a discriminating molecule or fragment is amolecule or fragment that, when detected, indicates presence orabundance of the above-identified transcript, cDNA, amplified nucleicacid, or protein. In some embodiments, the biomarker profile comprisesat least 2, 3, 4, 5, 6, 7, 8, 9, or 10 different biomarkers that eachcontains a probeset from any one of probeset collections listed in TableP.

TABLE P Exemplary probesets PROBESET COLLECTION IDENTITY OF PROBE INPROBESET COLLECTION 1 X206513_at, X214681_at, X235359_at, X221850_x_at,X213524_s_at, X225656_a, X200881_s_at, X229743_at, X215178_x_at,X215178_x_at, X216841_s_at, X216841_at, X244158_at, X238858_at,X205287_s_at, X233651_s_at, X229572_at, X214765_s_at. 2 X206513_at,X213524_s_at, X200881_s_at, X218992_at, X238858_at, X221123_x_at,X228402_at, X230585_at, X209304_x_at, X214681_at. 3 X204102_s_at,X236013_at, X213668_s_at, X1556639_at, X218220_at, X207860_at,X232422_at, X218578_at, X205875_s_at, X226043_at, X225879_at,X224618_at, X216316_x_at, X243159_x_at, X202200_s_at, X201936_s_at,X242492_at, X216609_at, X214328_s_at, X228648_at, X223797_at,X225622_at, X205988_at, X201978_s_at, X200874_s_at, X210105_s_at,X203913_s_at, X204225_at, X227587_at, X220865_s_at, X206682_at,X222664_at, X212264_s_at, X219669_at, X221971_x_at, X1554464_a_at,X242590_at, X227925_at, X221926_s_at, X202101_s_at, X211078_s_at,X44563_at, X206513_at, X215178_x_at, X235359_at, X225656_at, X244158_at,X214765_s_at, X229743_at, X214681.

In some embodiments, the methods or kits respectively described orreferenced in Section 5.2 and Section 5.3 use at least two differentbiomarkers listed in any one of the biomarker sets in Table Q. In aparticular embodiment, the biomarker profile comprises at least twodifferent biomarkers listed in any one of the biomarker sets in Table Q.The biomarker profile further comprises a respective correspondingfeature for the at least two biomarkers listed in any of the biomarkersets in Table Q. Generally, the at least two biomarkers are derived fromat least two different genes. In the case where a biomarker in the atleast two different biomarkers is listed in any one of biomarker sets ofTable Q, the biomarker can be, for example, a transcript made by thelisted gene, a complement thereof, or a discriminating fragment orcomplement thereof, or a cDNA thereof, or a discriminating fragment ofthe cDNA, or a discriminating amplified nucleic acid moleculecorresponding to all or a portion of the transcript or its complement,or a protein encoded by the gene, or a discriminating fragment of theprotein, or an indication of any of the above. Further still, thebiomarker can be, for example, a protein encoded by a gene listed in anyone of the biomarker sets in Table Q, or a discriminating fragment ofthe protein, or an indication of any of the above. Here, adiscriminating molecule or fragment is a molecule or fragment that, whendetected, indicates presence or abundance of the above-identifiedtranscript, cDNA, amplified nucleic acid, or protein. In accordance withthis embodiment, the biomarker profiles of the present invention can beobtained using any standard assay known to those skilled in the art, orin an assay described herein, to detect a biomarker. Such assays arecapable, for example, of detecting the products of expression (e.g.,nucleic acids and/or proteins) of a particular gene or allele of a geneof interest (e.g., a gene disclosed in any on of the biomarker sets ofTable Q). In one embodiment, such an assay utilizes a nucleic acidmicroarray. In some embodiments, a biomarker profile comprising at least2 or 3 different biomarkers from any one of the biomarker sets of TableQ is used.

TABLE Q Exemplary biomarker sets BIOMARKER SET NUMBER IDENTITY OFBIOMARKERS 1 IL18R1, ARG2, FCGR1A 2 ITGAM, TGFB1, TLR4, TNFSF, FCGR1A,IL18R1, ARG2 3 ARG2, TGFB1, MMP9, TLR4, ITGAM, IL18R1, TNFSF, IL1RN,FCGR1A 4 OSM, GADD45B, ARG2, IL18R1, TDRD9, PFKFB3, MAPK14, PRV1,MAP2K6, TNFRSF6, FCGR1A, INSL3, LY96, PSTPIP2, ANKRD22, TNFSF10,HLA-DRA, FNDC3B, TIFA, GADD45A, VNN1, ITGAM, BCL2A1, TLR4, TNFSF13B,SOCS3, IL1RN, CEACAM1, SOD2 5 ARG2, GADD45B, OSM, LY96, INSL3, ANKRD22,MAP2K6, PSTPIP2, TGFB1, GADD45B, TDRD9, MAP2K6, OSM, TNFSF10, ANKRD22

6. EXAMPLES

The following examples are representative of the embodiments encompassedby the present invention and in no way limit the subject embraced by thepresent invention. In the following examples, data was collected attwenty-four hour time intervals from each subject in a population ofsubjects. The population included two subject types. The first subjecttype was those that initially had SIRS and developed sepsis at aterminal time point in the analysis. The second subject type was thosethat initially had SIRS and did not develop sepsis at the terminal timepoint in the analysis. For subjects that initially had SIRS anddeveloped sepsis, a T⁻¹² time point was defined as the time frameimmediate prior to the onset of clinically-diagnosed sepsis. Inpractice, the T⁻¹² time point for each respective sepsis subject was theday the last blood sample was collected from the respective subjectprior to being diagnosed with sepsis.

For each time point, two types of analyses were performed, a static anda baseline analysis. In the static analysis, only data from a singletime point was considered. In particular, univariate and/or multivariatetechniques were used to identify biomarkers whose abundance oncorresponding microarray probesets on the U133 plus 2.0 (Affymetrix,Santa Clara, Calif.) discriminate between those subjects that developsepsis from those subjects that do not develop sepsis during the study.To illustrate, consider the case in which there are two subjects in thepopulation, subject A, who develops sepsis shortly after time periodT⁻¹², and subject B, who does not develop sepsis in any of the observedtime points. In the static analysis, microarray biomarker abundance datafrom the two subjects that was collected at a particular single timepoint is evaluated in order to identify those biomarkers that havedifferent abundance levels in the two subjects, as determined by a U133plus 2.0 microarray experiment. In fact, in the present examples, awhole population of subjects of type A and type B are evaluated andparametric and/or nonparametric statistical techniques are used toidentify those biomarkers whose abundance levels discriminate betweensubjects that develop sepsis at some point during the observation periodand subjects that do not develop sepsis during the observation period.Here, an observation period refers to a time period that was a matter ofhours, days, or weeks.

In addition to static analyses, baseline analyses were performed in theexamples below. In a baseline analysis, rather than identifyingbiomarkers whose corresponding features (e.g. abundance value) at asingle time point discriminate between sepsis subjects (subjects thatdevelop sepsis at some point during the observation time period) andsubjects that do not develop sepsis during the observed time frame,biomarkers whose change in abundance value across two or more timepoints discriminates between the two populations types were identified.For example, again consider subject A, who develops sepsis shortly aftertime period T⁻¹², and subject B, who does not develop sepsis in any ofthe observed time points. In the basesline analysis, what were neededare biomarker abundance values for each subject from two different timepoints (e.g., time point 1 and time point 2). For each respectivebiomarker considered, the difference in the abundance of the biomarkerat the two different time points was computed. These differentialabundances from each of the subjects is then used to determine whichcorresponding biomarkers, expressed as a differential between twodifferent time points, discriminate between subjects that develop sepsisduring the observation period and subjects that do not develop sepsisduring the observation time period.

6.1 Data Collection

SIRS positive subjects admitted to an ICU were recruited for the study.Subjects were eighteen years of age or older and gave informed consentto comply with the study protocol. Subjects were excluded from the studyif they were (i) pregnant, (ii) taking antibiotics to treat a suspectedinfection, (iii) were taking systemic corticosteroids (total dosagegreater than 100 mg hydrocortisone or equivalent in the past 48 hoursprior to study entry), (iv) had a spinal cord injury or other illnessrequiring high-dose corticosteroid therapy, (v) pharmacologicallyimmunosuppressed (e.g., azathioprine, methotrexate, cyclosporin,tacrolimus, cyclophosphamide, etanercept, anakinra, infliximab,leuflonamide, mycophenolic acid, OKT3, pentoxyphylin, etc.), (vi) werean organ transplant recipient, (vii) had active or metastatic cancer,(viii) had received chemotherapy or radiation therapy within 8 weeksprior to enrollment, and/or (ix) had taken investigational use drugswithin thirty days prior to enrollment.

In the study SIRS criteria were evaluated daily. APACHE II and SOFAscoring was performed following ICU admission. APACHE II is a system forrating the severity of medical illness. APACHE stands for “AcutePhysiology And Chronic Health Evaluation,” and is most frequently usedto predict in-hospital death for patients in an intensive care unit.See, for example, Gupta et al., 2004, Indian Journal of Medical Research119, 273-282, which is hereby incorporated herein by reference in itsentirety. SOFA is a test to measure the severity of sepsis. See, forexample, Vincent et al., 1996, Intensive Care Med. 22, 707-710, which ishereby incorporated herein by reference in its entirety. Patients weremonitored daily for up to two weeks for clinical suspicion of sepsisincluding, but not limited to, any of the following signs and symptoms:

-   -   pneumonia: temperature>38.3° C. or <36° C.+white blood cell        count (WBC)>12,000/mm³ or <4,000/mm³+new-onset of purulent        sputum+new or progressive infiltrate on chest radiograph (3 out        of 4 findings);    -   wound infection: temperature>38.3° C. or <36°        C.+pain+erythema+purulent discharge (3 out of 4 findings);    -   urinary tract infection: temperature>38.3° C. or WBC>12,000/mm³        or <4,000/mm³+bacteruria and pyuria (>10 WBC/hpf or positive        leukocyte esterase) (all findings);    -   line sepsis: temperature>38.3° C. or <36°        C.+erythema/pain/purulence at catheter exit site (3 out of 4        findings, including fever);    -   intra-abdominal abscess: temperature>38.3° C. or <36°        C.+WBC>12,000/mm³ or <4,000/mm³+radiographic evidence of fluid        collection (2 out of 3 criteria);    -   CNS Infection: temperature>38.3° C. or <36° C.+WBC>12,000/mm³ or        <4,000/mm³+CSF pleocytosis via LP or Ventricular drainage.

Blood was drawn daily for a minimum of four consecutive days beginningwithin 24 hours following study entry. Patients were followed and bloodsamples were drawn daily for a maximum of fourteen consecutive daysunless clinical suspicion of infection occurred. The maximum volume ofblood drawn from any one subject did not exceed 210 mL over the courseof a 14 day study maximum. Blood draws for the study were discontinuedif the loss of blood posed risk to the patient as defined by physician'sjudgment. Each patient had two Paxgene (RNA) tubes drawn on each day.One tube was used for the microarray analysis described in Section 6.2.The other tube was used for the RT-PCR analysis described in Section6.10.

6.2 Microarray Analysis

RNA was extracted from each blood sample described in Section 6.1,labeled, reversed transcribed to generate cDNA which was labeled, andthe labeled cDNA was hybridized to Affymetrix (Santa Clara, Calif.) U133plus 2.0 human genome chips containing 54,675 probesets. To enhancedetection sensitivity of the microarray, globin mRNA molecules wereremoved from the total RNA extracted from the blood samples using themethods described in, for example, U.S. Patent Publication 20050221310,filed Aug. 9, 2004, and Ser. No. 10/948,635, filed Sep. 24, 2004, bothentitled “Methods of Enhancing Gene Expression Analysis,” each of whichis incorporated by reference herein in its entirety. The U133 plus 2.0has 62 probesets designed for special functions, such as measuringsupplementally added transcripts. This leaves 54,613 probesets designedspecifically for the detection of human genes. The Affymetrix humangenome U133 (HG-U133) set, consisting of two microarrays, containsalmost 45,000 probesets representing more than 39,000 transcriptsderived from approximately 33,000 human genes. This set design usessequences selected from GenBank, dbEST, and RefSeq. As used herein, theabundance value measured for each of the biomarkers that bind to theseprobesets is referred to as a feature. The examples below discussabundance values of biomarkers that bind to particular probesets in theU133 plus 2.0 human genome chip.

6.3 Static T⁻³⁶ Data Analysis

In one experiment, a T⁻³⁶ static analysis was performed. In the T⁻³⁶static analysis, biomarkers features are determined using a specificblood sample, designated the T⁻³⁶ blood sample, from each subject in atraining population. The identity of this specific blood sample fromeach respective subject in the training population is dependent uponwhether the subject was a SIRS subject (did not develop sepsis duringthe observation period) or was a sepsis subject (did develop sepsisduring the observation period). In the case of a sepsis subject, theT⁻³⁶ sample is defined as the second to last blood sample taken from thesubject before the subject acquired sepsis. Identification of T⁻³⁶samples in the SIRS subjects in the training population was morediscretionary than for the sepsis counterpart subjects because there wasno significant event in which the SIRS subjects became septic. Becauseof this, the identity of the T⁻³⁶ samples for the sepsis subjects in thetraining population was used to identify the T⁻³⁶ samples in the SIRSsubjects in the training population. Specifically, T⁻³⁶ time points(blood samples) for SIRS subjects in the training population wereidentified by “time-matching” a septic subject and a SIRS subject. Forexample, consider the case in which a subject that entered the studybecame clinically-defined as septic on their sixth day of enrollment.For this subject, T⁻³⁶ is day four of the study, and the T⁻³⁶ bloodsample is the blood sample that was obtained on day four of the study.Likewise, T⁻³⁶ for the SIRS subject that was matched to this sepsissubject is deemed to be day four of the study on this paired SIRSsubject.

Although SIRS subjects did not progress on to develop sepsis, they didhave changes in their expressed genes (and proteins, etc.) over time.Thus, a one-to-one time matching of sepsis subjects to SIRS subjects forthe purpose of obtaining a relevant set of T⁻³⁶ blood samples from theSIRS subjects was sought in the manner described above. Just as subjectswho progressed to become septic did so at varying rates, this timematching was done to mimic feature variability in SIRS subjects. Whiletime matching between arbitrary pairs of SIRS and sepsis subjects wasdone to identify T⁻³⁶ blood samples for as many of the SIRS subjects inthe training population as possible, in some instances, T⁻³⁶ samplesfrom SIRS subjects had to be selected from time points based on sampleavailability.

For the T⁻³⁶ static analysis there were 54,613 biomarkers measured on 84samples for a total of 84 corresponding microarray experiments from 84different subjects. Each sample was collected from a different subjectin the population of 84 subject. Of the 54,613 probesets measured ineach microarray experiment, 30,464 were transformed by logtransformations. The log transformation is described in Draghici, 2003,Data Analysis Tools for DNA Microarrays, Chapman & Hall/CRC, Boca Raton,pp. 309-311, which is hereby incorporated by reference in its entirety.Further, of the 54,613 probesets in each microarray experiment, 2317were transformed by a square root transformation. The square roottransformation is described in Ramdas, 2001, Genome Biology 2,47.1-47.7, which is hereby incorporated by reference in its entirety.The remaining 21,832 probesets in each microarray experiment were nottransformed.

The 84 member population was initially split into a training set (n=64)and a validation set (n=20). The training set was used to estimate theappropriate classification algorithm parameters while the trainedalgorithm was applied to the validation set to independently assessperformance. Of the 64 training samples, 35 were Sepsis, meaning thatthe subjects developed sepsis at some point during the observation timeperiod, and 29 were SIRS, meaning that they did not develop sepsisduring the observation time period. Table 1 provides distributions ofthe race, gender and age for these samples.

TABLE 1 Distributions of the race, gender, and age for the training dataGroup Gender Black Caucasian Other Sepsis Male 10 13 1 Female 0 10 1SIRS Male 5 17 0 Female 0 7 0 Group Minimum Mean Median Maximum Sepsis18 42 41 80 SIRS 18 43 40 90

For the 20 validation samples, 9 were Sepsis and 11 were SIRS. Table 2provides distributions of the race, gender and age for these samples.

TABLE 2 Distributions of the race, gender, and age for the validationdata Group Gender Black Caucasian Other Sepsis Male 1 7 0 Female 0 3 0SIRS Male 0 6 0 Female 0 3 0 Group Minimum Mean Median Maximum Sepsis 1841.8 43 81 SIRS 19 47.7 51 77

Each sample in the training data was randomly assigned to one of tengroups used for cross-validation. The number of training samples inthese groups ranged from 6 to 7. The samples were assigned in way thatattempted to balance the number of sepsis and SIRS samples across folds.As described in more detail below, several different methods were usedto judge whether select biomarkers, which bind to particular probesetsin the microarray, discriminate between the Sepsis and SIRS groups.

Wilcoxon and Q-Value Tests.

The first method used to identify discriminating biomarkers was aWilcoxon test (unadjusted). The Wilcoxon test is a distribution-freetest is resistant to extreme values. The Wilcoxon test is described inAgresti, 1996, An Introduction to Categorical Data Analysis, John Wiley& Sons, Inc, New York, Chapter 2, which is hereby incorporated byreference in its entirety. The Wilcoxon test produces a p value. Theabundance value for a given biomarker from all samples in the trainingdata is subjected to the Wilcoxon test. The Wilcoxon test considers bothgroup classification (sepsis versus SIRS) and abundance value in orderto compute a p value for the given biomarker. The p value provides anindication of how well the abundance value for the given biomarkeracross the samples collected in the training set discriminates betweenthe sepsis and SIRS state. When the p value is less than a specificconfidence level, such as 0.05, an inference is made that the biomarkerdiscriminates between Sepsis and SIRS. There were 9520 significantbiomarkers using this method (see Table 3).

The second method used to identify discriminating biomarkers was theWilcoxon Test (adjusted). Due to the large number of biomarkers, 54613,and the relatively small number of samples, 84, there was a high risk offinding falsely significant biomarkers. An adjusted p-value was used tocounter this risk. In particular, the method of Benjamini and Hochberg,1995, J.R. Statist. Soc. B 57, pp 289-300, which is hereby incorporatedby reference in its entirety, was used to control the false discoveryrate. Here, the false discovery rate is defined as the number ofbiomarkers truly significant divided by the number of biomarkersdeclared significant. For example, if the adjusted p-value is less than0.05, there is a 5% chance that the biomarker is a false discovery.Results using this test are reported in Table 3. There were 1618significant biomarkers using this method (see Table 3). As used, herein,a biomarker is considered significant if the feature valuescorresponding to the biomarker have a p-value of less than 0.05 asdetermined by the Wilcoxon test (adjusted).

The third method used to identify discriminating biomarkers was the useof Q values. Q-values are described in Storey, 2002, J.R. Statist. Soc.B 64, Part 3, pp. 479-498, which is hereby incorporated by reference inits entirety. The biomarkers are ordered by their q-values and if abiomarker has a q-value of X, then this biomarker and all others moresignificant have a combined false discovery rate of X. However, thefalse discovery rate for any one biomarker may be much larger. Therewere 2431 significant markers using this method (see Table 3).

TABLE 3 Cumulative number of significant calls for the three methods.Note that all 84 samples (training and validation) were used to compareconverters and nonconverters. Missing biomarker values were not includedin the analyses. ≦1e−04 ≦0.001 ≦0.01 ≦0.025 ≦0.05 ≦0.1 ≦1 p-value 0 13624210 6637 9520 13945 54613 (un- adjusted) p-value 0 0 0 584 1618 331554613 (adjusted) q-value 0 0 0 1055 2431 4785 54613

CART.

In addition to analyzing the microarray data using Wilcoxon test andQ-value tests in order to identify biomarkers that discriminate betweenthe sepsis and SIRS subpopulations in the training set, classificationand regression tree (CART) analysis was used. CART is described inSection 5.5.1, above. Specifically, the data summarized above was usedto predict the disease state by iteratively partitioning the data basedon the best single-variable (feature of biomarker across training set)split of the data. In other words, at each stage of the tree buildingprocess, the biomarker whose abundance value across the trainingpopulation best discriminates between the sepsis and SIRS population wasinvoked as a decision branch. Cross-validation was carried out, with theoptimal number of splits estimated independently in each of the 10iterations. The final tree is depicted in FIG. 1. In FIG. 1, decision102 makes a decision based on the abundance of the biomarker that bindsto X204319_s_at. If the biomarker that binds to X204319_s_at has anabundance that is greater than 2.331 units in a biological sample from asubject to be diagnosed (test biological sample), then control passes todecision 104. If, on the other hand, the biomarker that binds toprobeset X204319_s_at has abundance that is less than 2.331 units in thetest biological sample, decision control passes to decision 106.Decisions are made in this manner until a terminal leaf of the decisiontree is reached, at which point diagnoses of sepsis or SIRS is made. Thedecision tree in FIG. 1 makes use of the biomarkers that bind to thefollowing five probesets: X204319_s_at, X1562290_at, X1552501_a_at,X1552283_s_at, and X117_at.

FIG. 2 shows the distribution of the biomarkers that bind to the fiveprobesets used in the decision tree between the sepsis and SIRS groupsin the training data set. In FIG. 2, the top of each box denotes the75^(th) percentile of the data across the training set and the bottom ofeach box denotes the 25^(th) percentile, and the median value for eachbiomarker across the training set is drawn as a line within each box.The confusion matrix for the training data where the predictedclassifications were made from the cross-validated model is given inTable 4. From this confusion matrix, the overall accuracy was estimatedto be 70.3% with a 95% confidence interval of 57.6% to 81.1%. Theestimated sensitivity was 60% and the estimated specificity was 82.8%.

TABLE 4 Confusion matrix for training samples using the cross-validatedCART algorithm of FIG. 1. True Diagnosis Predicted Sepsis SIRS Sepsis 215 SIRS 14 24

For the 20 validation samples held back from training data set, theoverall accuracy was estimated to be 70% with a 95% confidence intervalof 45.7% to 88.1%, sensitivity 88.9% and specificity 54.5%. Table 5shows the confusion matrix for the validation samples.

TABLE 5 Confusion matrix for validation samples using thecross-validated CART algorithm of FIG. 1. True Diagnosis PredictedSepsis SIRS Sepsis 8 5 SIRS 1 6

Random Forests.

Another decision rule that can be developed using biomarkers of thepresent invention is a Random Forests decision tree. Random Forests is atree based method that uses bootstrapping instead of cross-validation.For each iteration, a random sample (with replacement) is drawn and thelargest tree possible is grown. Each tree receives a vote in the finalclass prediction. To fit a random forest, the number of trees (e.g.bootstrap iterations) is specified. No more than 500 were used in thisexample, but at least 50 are needed for a burn-in period. The number oftrees was chosen based on the accuracy of the training data. For thisdata, 500 trees were used to train the algorithm (see FIG. 3). In FIG.3, curve 302 is a smoothed estimate of overall accuracy as a function oftree number. Curve 304 is a smoothed curve of tree sensitivity as afunction of tree number. Curve 306 is a smoothed curve of treespecificity as a function of tree number. Using this algorithm, 901biomarkers had non-zero importance and were used in the model. Therandom forest algorithm gauges biomarker importance by the averagereduction in the training accuracy. The biomarkers were ranked by thismethod and are shown in FIG. 4. In FIG. 4, the biomarkers are labeled bythe name of the U133 plus 2.0 probeset to which they bind. The figureonly reflects the 50 most important biomarkers found by using RandomForest analysis. However, 901 biomarkers were actually found to havediscriminating significance. The random forest method uses a number ofdifferent decision trees. A biomarker is considered to havediscriminating significance if it served as a decision branch of adecision tree from a significant random forest analysis. As used herein,a significant random forest analysis is one where the lower 95%confidence interval on accuracy by cross validation on a training dataset is greater than 50% and the point estimate for accuracy on avalidation set is greater than 65%.

The predicted confusion matrix for the training dataset using thedecision tree developed using the Random Forest method is given in Table6. From this confusion matrix, the overall accuracy was estimated to be68.8% (confidence intervals cannot be computed when using the bootstrapaccuracy estimate). The estimated sensitivity was 74.3% and theestimated specificity was 62.1%.

TABLE 6 Confusion matrix for training samples against the decision treedeveloped using the Random Forest method. True Diagnosis PredictedSepsis SIRS Sepsis 18 9 SIRS 11 26

For the 20 validation samples held back from training, the overallaccuracy was estimated to be 65% with a 95% confidence interval of 40.8%to 84.6%, sensitivity 66.7% and specificity 63.6%. Table 7 shows theconfusion matrix for the validation samples.

TABLE 7 Confusion matrix for the 20 validation samples against thedecision tree developed using the Random Forest method. True DiagnosisPredicted Sepsis SIRS Sepsis 6 4 SIRS 3 7

PAM.

Yet another decision rule developed using the biomarkers of the presentinvention is predictive analysis of microarrays (PAM), which isdescribed in Section 5.5.2, above. In this method, a shrinkage parameterthat determines the number of biomarkers used to classify samples isspecified. This parameter was chosen via cross-validation. There were nobiomarkers with missing values. Based on cross-validation, the optimalthreshold value was 2.07, corresponding to 258 biomarkers. FIG. 5 showsthe accuracy across different thresholds. In FIG. 5, curve 502 is theoverall accuracy (with 95% confidence interval bars). Curve 504 showsdecision rule sensitivity as a function of threshold value. Curve 506shows decision rule specificity as a function of threshold value. Usingthe threshold of 2.07, the overall accuracy for the training samples wasestimated to be 73.4% with 95% a confidence interval of 61.4% to 82.8%.The estimated sensitivity was 79.3% and the estimated specificity was68.6%.

TABLE 8 Confusion matrix for training samples using cross-validated PAMalgorithm True Diagnosis Predicted Sepsis SIRS Sepsis 23 11 SIRS 6 24

For the twenty validation samples held back from training, the overallaccuracy was estimated to be 70% with a 95% confidence interval of 45.7%to 88.1%, sensitivity 66.7% and specificity 72.7%. Table 9 shows theconfusion matrix for the validation samples.

TABLE 9 Confusion matrix for training samples using cross-validated PAMalgorithm True Diagnosis Predicted Sepsis SIRS Sepsis 6 3 SIRS 3 8

FIG. 6 shows the selected biomarkers, ranked by their relativediscriminatory power, and their relative importance in the model. FIG. 6only shows the fifty most important biomarkers found using the PAManalysis. However, 258 important biomarkers were found. The biomarkersin FIG. 6 are labeled based upon the U133 plus 2.0 probeset to whichthey bind.

FIG. 7 provides a summary of the CART, PAM, and random forestsclassification algorithm (decision rule) performance and associated 95%confidence intervals. Fifty distinct biomarkers were selected fromacross all the algorithms illustrated in FIG. 7. FIG. 8 illustrates thenumber of times that common biomarkers were selected across thetechniques of Wilcoxon (adjusted), CART, PAM, and RF. FIG. 9 illustratesan overall ranking of biomarkers for the T-36 data set. For the selectedbiomarkers, the x-axis depicts the percentage of times that it wasselected. Within the percentage of times that biomarkers were selected,the biomarkers are ranked. The biomarkers in FIG. 7 are labeled basedupon the probeset (oligonucleotide identity) to which they bind.

6.4 Static T⁻¹² Data Analysis

In another experiment, a T⁻¹² static analysis was performed. In the T⁻¹²static analysis, biomarkers features were measured using a specificblood sample, designated the T⁻¹² blood sample, obtained from eachsubject in the training population. The identity of this specific bloodsample from a given subject in the training population was dependentupon whether the subject was a SIRS subject (did not develop sepsisduring the observation period) or a sepsis subject (did develop sepsisduring the observation period). In the case of a sepsis subject, theT⁻¹² sample was defined as the last blood sample taken from the subjectbefore the subject acquired sepsis. Identification of T⁻¹² samples inthe SIRS subjects in the training population was more discretionary thanfor the sepsis counterpart subjects because there was no significantevent in which the SIRS subjects became septic. Because of this, theidentity of the T⁻¹² samples for the sepsis subjects in the trainingpopulation was used to identify the T⁻¹² samples in the SIRS subjects inthe training population. Specifically, T⁻¹² time points (blood samples)for SIRS subjects in the training population were identified by“time-matching” a septic subject and a SIRS subject. For example,consider the case in which a subject that entered the study becameclinically-defined as septic on their sixth day of enrollment. For thissubject, T⁻¹² was day five of the study (1-24 hours prior to sepsis),and the T⁻¹² blood sample was the blood sample that was obtained on dayfive of the study. Likewise, T⁻¹² for the SIRS subject that was matchedto this sepsis subject was deemed to be day five of study on this pairedSIRS subject. While time matching between arbitrary pairs of SIRS andsepsis subjects was done to identify T⁻¹² blood samples for as many ofthe SIRS subjects in the training population as possible, in someinstances, T⁻¹² samples from SIRS subjects had to be selected from thetime points based on sample availability.

For the T⁻¹² static analysis, there were 54,613 biomarkers measured on90 samples for a total of 90 corresponding microarray experiments from90 different subjects. Each sample was collected from a different memberthe population. Of the 54,613 probesets in each microarray experiment,31,047 were transformed by log transformations. Further, of the 54,613probesets in each microarray experiment, 2518 were transformed by asquare root transformation. The remaining 21,048 probesets in eachmicroarray experiment were not transformed.

The 90 member population was initially split into a training set (n=69)and a validation set (n=21). The training set was used to estimate theappropriate classification algorithm parameters while the trainedalgorithm was applied to the validation set to independently assessperformance. Of the 69 training samples, 34 were labeled Sepsis, meaningthat the subjects developed sepsis at some point during the observationtime period, and 35 were SIRS, meaning that they did not develop sepsisduring the observation time period. Table 10 provides distributions ofthe race, gender and age for these samples.

TABLE 10 Distributions of the race, gender, and age for the trainingdata Group Gender Black Caucasian Other Sepsis Male 9 13 1 Female 0 10 1SIRS Male 5 20 0 Female 0 10 0 Group Minimum Mean Median Maximum Sepsis18 42.1 39 80 SIRS 18 44.1 40 90

For the 21 validation samples, 11 were labeled Sepsis and 10 werelabeled SIRS. Table 11 provides distributions of the race, gender andage for these samples.

TABLE 11 Distributions of the race, gender, and age for the validationdata Group Gender Black Caucasian Other Sepsis Male 0 7 0 Female 0 3 0SIRS Male 2 6 0 Female 0 3 0 Group Minimum Mean Median Maximum Sepsis 1843.3 40 81 SIRS 19 53 52 85

Each sample in the training data was randomly assigned to one of tengroups used for cross-validation. The number of training samples inthese groups ranged from 6 to 8. The samples were assigned in way thatattempted to balance the number of sepsis and SIRS samples across folds.As described in more detail below, several different methods were usedto judge whether select biomarkers discriminate between the Sepsis andSIRS groups.

Wilcoxon and Q-Value Tests.

The first method used to identify discriminating biomarkers was aWilcoxon test (unadjusted). The abundance value for a given biomarkeracross the samples in the training data was subjected to the Wilcoxontest. The Wilcoxon test considers both group classification (sepsisversus SIRS) and abundance value in order to compute a p value for thegiven biomarker. The p value provides an indication of how well theabundance value for the given biomarker across the samples collected inthe training set discriminates between the sepsis and SIRS state. Thelower the p value, the better the discrimination. When the p value isless than a specific confidence level, such as 0.05, an inference ismade that the biomarker discriminates between the sepsis and SIRSphenotype. There were 19,791 significant biomarkers using this method(see Table 12).

The second method used to identify discriminating biomarkers was theWilcoxon Test (adjusted). Due to the large number of biomarkers, 54613,and the relatively small number of samples, 90, there was a high risk offinding falsely significant biomarkers. An adjusted p-value was used tocounter this risk. In particular, the method of Benjamini and Hochberg,1995, J.R. Statist. Soc. B 57, pp 289-300, which is hereby incorporatedby reference in its entirety, was used to control the false discoveryrate. Here, the false discovery rate is defined as the number ofbiomarkers truly significant divided by the number of biomarkersdeclared significant. For example, if the adjusted p-value is less than0.05, there is a 5% chance that the biomarker is a false discovery.Results using this test are reported in Table 12. There were 11851significant biomarkers using this method (see Table 12). As used,herein, a biomarker is considered significant if it has a p-value ofless than 0.05 as determined by the Wilcoxon test (adjusted).

The third method used to identify discriminating biomarkers was the useof Q values. In such an approach, the biomarkers are ordered by theirq-values and if a respective biomarker has a q-value of X, thenrespective biomarker and all others more significant have a combinedfalse discovery rate of X. However, the false discovery rate for any onebiomarker may be much larger. There were 11851 significant biomarkersusing this method (see Table 12).

TABLE 12 Cumulative number of significant calls for the three methods.Note that all 90 samples (training and validation) were used to compareSepsis and SIRS groups. Missing biomarker feature values were notincluded in the analyses. ≦1e−04 ≦0.001 ≦0.01 ≦0.025 ≦0.05 ≦0.1 ≦1p-value 0 5417 11537 15769 19791 24809 54613 (un- adjusted) p-value 0 05043 8374 11851 16973 54613 (adjusted) q-value 0 0 7734 12478 1782024890 54613

CART.

In addition to analyzing the microarray data using Wilcoxon test andQ-value tests in order to identify biomarkers that discriminate betweenthe sepsis and SIRS subpopulations in the training set, classificationand regression tree (CART) analysis was used. CART is described inSection 5.5.1, above. Specifically, the data summarized above was usedto predict the disease state by iteratively partitioning the data basedon the best single-variable split of the data. In other words, at eachstage of the tree building process, the biomarker whose expressionvalues across the training population best discriminate between thesepsis and SIRS population was invoked as a decision branch.Cross-validation was carried out, with the optimal number of splitsestimated independently in each of the 10 iterations. The final tree isdepicted in FIG. 10. In FIG. 10, decision 1002 makes a decision based onthe abundance of the biomarker that binds to probeset X214681_at. Ifbiomarker X214681_at has an abundance that is greater than 7.862 unitsin a biological sample from a subject to be diagnosed (test biologicalsample), than control passes to decision 1004. If, on the other hand, ifthe biomarker that binds to probeset (U133 plus 2.0 oligonucleotide)X214681_at has an abundance that is less than 7.862 units in the testbiological sample, decision control passes to decision 1006. Decisionsare made in this manner until a terminal leaf of the decision tree isreached, at which point diagnoses of sepsis or SIRS is made. Thedecision tree in FIG. 10 makes use of the biomarkers that bind to thefollowing four probesets: X214681_at, X1560432_at, X230281_at, andX1007_s_at.

FIG. 11 shows the distribution of the four biomarkers used in thedecision tree between the sepsis and SIRS groups in the training dataset. In FIG. 11, the top of each box denotes the 75^(th) percentile ofthe data across the training set and the bottom of each box denotes the25^(th) percentile, and the median value for each biomarker across thetraining set is drawn as a line within each box. The biomarkers arelabeled in FIG. 11 based on the identity of the U133 plus 2.0 probes towhich they bind). The confusion matrix for the training data where thepredicted classifications were made from the cross-validated model isgiven in Table 13. From this confusion matrix, the overall accuracy wasestimated to be 65.2% with a 95% confidence interval of 52.8% to 76.3%.The estimated sensitivity was 61.8% and the estimated specificity was68.6%.

TABLE 13 Confusion matrix for training samples using the cross-validatedCART algorithm of FIG. 10 True Diagnosis Predicted Sepsis SIRS Sepsis 2111 SIRS 13 24

For the 21 validation samples held back from training data set, theoverall accuracy was estimated to be 71.4% with a 95% confidenceinterval of 47.8% to 88.7%, sensitivity 90.9% and specificity 50%. Table14 shows the confusion matrix for the validation samples.

TABLE 14 Confusion matrix for validation samples using thecross-validated CART algorithm of FIG. 10 True Diagnosis PredictedSepsis SIRS Sepsis 10 5 SIRS 1 5

Random Forests.

Another decision rule that can be developed using biomarkers of thepresent invention is a Random Forests decision tree. Random Forests is atree based method that uses bootstrapping instead of cross-validation.For each iteration, a random sample (with replacement) is drawn and thelargest tree possible is grown. Each tree receives a vote in the finalclass prediction. To fit a random forest, the number of trees (e.g.bootstrap iterations) is specified. No more than 500 were used in thisexample, but at least 50 are needed for a burn-in period. The number oftrees was chosen based on the accuracy of the training data. For thisdata, 439 trees were used to train the algorithm (see FIG. 12). In FIG.12, curve 1202 is a smoothed estimate of overall accuracy as a functionof tree number. Curve 1204 is a smoothed curve of tree sensitivity as afunction of tree number. Curve 1206 is a smoothed curve of treespecificity as a function of tree number. Using this algorithm, 845biomarkers had non-zero importance and were used in the model. Therandom forest algorithm gauges biomarker importance by the averagereduction in the training accuracy. The biomarkers were ranked by thismethod and are shown in FIG. 13. The figure only reflects the 50 mostimportant biomarkers found by using Random Forest analysis. However, 845biomarkers were actually found to have discriminating significance. Therandom forest method uses a number of different decision trees. Abiomarker is considered to have discriminating significance if it servedas a decision branch of a decision tree from a significant random forestanalysis. As used herein, a significant random forest analysis is onewhere the lower 95% confidence interval on accuracy by cross validationon a training data set is greater than 50% and the point estimate foraccuracy on a validation set is greater than 65%.

The predicted confusion matrix for the training dataset using thedecision tree developed using the Random Forest method is given in Table15. From this confusion matrix, the overall accuracy was estimated to be75.4% (confidence intervals cannot be computed when using the bootstrapaccuracy estimate). The estimated sensitivity was 73.5% and theestimated specificity was 77.1%.

TABLE 15 Confusion matrix for training samples against the decision treedeveloped using the Random Forest method. True Diagnosis PredictedSepsis SIRS Sepsis 27 9 SIRS 8 25

For the 21 validation samples held back from training, the overallaccuracy was estimated to be 95.2% with a 95% confidence interval of76.2% to 99.9%, sensitivity 100% and specificity 90%. Table 16 shows theconfusion matrix for the validation samples.

TABLE 16 Confusion matrix for the 20 validation samples against thedecision tree developed using the Random Forest method. True DiagnosisPredicted Sepsis SIRS Sepsis 11 1 SIRS 0 9

MART.

Multiple Additive Regression Trees (MART), also known as “gradientboosting machines,” was used to simultaneously assess the importance ofbiomarkers and classify the subject samples. Several fitting parametersare specified in this approach including (i) number of trees, (ii) stepsize (commonly referred to as “shrinkage”), and (iii) degree ofinteraction (related to the number of splits for each tree). Moreinformation on MART is described in Section 5.5.4 above. The degree ofinteraction was set to 1 to enforce an additive model (e.g. each treehas one split and only uses one biomarker).

Estimating interactions may require more data to function well. The stepsize was set to 0.05 so that the model complexity was dictated by thenumber of trees. The optimal number of trees was estimated by leavingout a random subset of cases at each fitting iteration, then assessingquality of prediction on that subset. After fitting more trees than werewarranted, the point at which prediction performance stopped improvingwas estimated as the optimal point.

The estimated model used 28 trees and 17 biomarkers across all trees.The MART algorithm also provides a calculation of biomarker importance(summing to 100%), which are given in FIG. 14. Biomarkers with zeroimportance were excluded. In FIG. 14, biomarkers are labeled by the U133plus 2.0 oligonucleotide to which they bind. FIG. 15 shows thedistribution of the selected biomarkers between the Sepsis and SIRSgroups. In FIG. 15, biomarkers are labeled by the U133 plus 2.0oligonucleotide to which they bind.

Cross-validation was carried out, with the optimal number of treesestimated independently in each of the 10 iterations. The confusionmatrix for the training data where the predicted classifications weremade from the cross-validated model is given in Table 17. From thisconfusion matrix, the overall accuracy was estimated to be 76.8% with a95% confidence interval of 65.1% to 86.1%. The estimated sensitivity was76.5% and the estimated specificity was 77.1%.

TABLE 17 Confusion matrix for the training samples using thecross-validated MART algorithm. True Diagnosis Predicted Sepsis SIRSSepsis 26 8 SIRS 8 27

For the 21 validation samples held back from training, the overallaccuracy was estimated to be 85.7% with a 95% confidence interval of63.7% to 97%, sensitivity 80% and specificity 90.9%. Table 18 shows theconfusion matrix for the validation samples.

TABLE 18 Confusion matrix for the validation samples using the MARTalgorithm. True Diagnosis Predicted Sepsis SIRS Sepsis 8 1 SIRS 2 10

PAM.

Yet another decision rule developed using biomarkers of the presentinvention is predictive analysis of microarrays (PAM), which isdescribed in Section 5.5.2, above. In this method, a shrinkage parameterthat determines the number of biomarkers used to classify samples isspecified. This parameter was chosen via cross-validation. There were nobiomarkers with missing values. Based on cross-validation, the optimalthreshold value was 2.1, corresponding to 820 biomarkers. FIG. 16 showsthe accuracy across different thresholds. In FIG. 16, curve 1602 is theoverall accuracy (with 95% confidence interval bars). Curve 1604 showsdecision rule sensitivity as a function of threshold value. Curve 1606shows decision rule specificity as a function of threshold value. Usingthe threshold of 2.1, the overall accuracy for the training samples wasestimated to be 80.9% with a 95% confidence interval of 73.4% to 86.7%.The estimated sensitivity was 85.7% and the estimated specificity was76.5%. Table 19 shows the confusion matrix for the training data wherethe predicted classifications were made from the cross-validated models.

TABLE 19 Confusion matrix for training samples using cross-validated PAMalgorithm True Diagnosis Predicted Sepsis SIRS Sepsis 30 8 SIRS 5 26

For the 21 validation samples held back from training, the overallaccuracy was estimated to be 95.2% with a 95% confidence interval of76.2% to 99.9%, sensitivity 100% and specificity 90%. Table 20 shows theconfusion matrix for the validation samples.

TABLE 20 Confusion matrix for validation samples using cross-validatedPAM algorithm True Diagnosis Predicted Sepsis SIRS Sepsis 11 1 SIRS 0 9

FIG. 17 shows the selected biomarkers, ranked by their relativediscriminatory power, and their relative importance in the model. FIG.17 only shows the fifty most important biomarkers found using the PAManalysis. However, 820 important biomarkers were found. In FIG. 17,biomarkers are labeled by the U133 plus 2.0 oligonucleotide to whichthey bind.

FIG. 18 provides a summary of the CART, MART, PAM, and random forests(RF) classification algorithm (decision rule) performance and associated95% confidence intervals. Fifty distinct biomarkers were selected fromacross all the algorithms illustrated in FIG. 18. The identity of thesefifty selected features is shown in FIG. 20. FIG. 19 illustrates thenumber of times that common biomarkers were selected across thetechniques of CART, MART, PAM, RF, and Wilcoxon (adjusted). FIG. 20illustrates an overall ranking of biomarkers for the T⁻¹² data set. Forthe selected biomarkers, the x-axis depicts the percentage of times thatit was selected. Within the percentage of times that biomarkers wereselected, the biomarkers are ranked. In FIG. 20, biomarkers are labeledby the U133 plus 2.0 oligonucleotide to which they bind.

6.5 Baseline T⁻¹² Data Analysis

In another example, a baseline T⁻¹² analysis was performed. Featurevalues for biomarkers in this example were computed as the differentialbetween two time points. The two time points for each respective subjectin a training population were (i) the T⁻¹² time point and (ii) the firstmeasurement, T_(first), taken of the respective subject. It will beappreciated that T_(first) could differ across the training population.For example, in some subjects, T_(first) was two days before T⁻¹², insome subjects T_(first) was three days before T⁻¹², and so forth. Toillustrate the computation of a feature value in accordance with theT⁻¹² baseline analysis, consider the case in which biomarker A wasevaluated. To compute a feature value for biomarker A for the purposesof the baseline T⁻¹² analysis, the abundance of biomarker A in the T⁻¹²blood sample for a respective subject in the training population[A]_(T-12), was obtained. Further, the abundance of biomarker A from thefirst blood sample taken for the respective subject, [A]_(first), wasobtained. The feature value for A for this respective subject was thencomputed as ΔA=[A]_(T-12)−[A]_(first). This calculation was repeated foreach subject in the training population and for each biomarker underconsideration.

For the baseline T⁻¹² analysis, there were 54,613 probesets measured on89 samples for a total of 89 corresponding microarray experiments from89 different subjects. Each sample was collected from a different memberof the population. Of the 54,613 probesets in each microarrayexperiment, 31,047 were transformed by log transformations. Further, ofthe 54,613 probesets in each microarray experiment, 2518 weretransformed by a square root transformation. The remaining 21,048probesets in each microarray experiment were not transformed.

The 89 member population was initially split into a training set (n=68)and a validation set (n=21). The training set was used to estimate theappropriate classification algorithm parameters while the trainedalgorithm was applied to the validation set to independently assessperformance. Of the 68 training samples, 33 were Sepsis, meaning thatthe subjects developed sepsis at some point during the observation timeperiod, and 35 were SIRS, meaning that they did not develop sepsisduring the observation time period. Table 21 provides distributions ofthe race, gender and age for these samples.

TABLE 21 Distributions of the race, gender, and age for the trainingdata Group Gender Black Caucasian Other Sepsis Male 9 12 1 Female 0 10 1SIRS Male 5 20 0 Female 0 10 0 Group Minimum Mean Median Maximum Sepsis18 42.7 39 80 SIRS 18 44.1 40 90

For the 21 validation samples, 11 were Sepsis and 10 were SIRS. Table 22provides distributions of the race, gender and age for these samples.

TABLE 22 Distributions of the race, gender, and age for the validationdata Group Gender Black Caucasian Other Sepsis Male 0 7 0 Female 0 3 0SIRS Male 2 6 0 Female 0 3 0 Group Minimum Mean Median Maximum Sepsis 1843.3 40 81 SIRS 19 53 52 85

Each sample in the training data was randomly assigned to one of tengroups used for cross-validation. The number of training samples inthese groups ranged from 6 to 8. The samples were assigned in way thatattempted to balance the number of sepsis and SIRS samples across folds.As described in more detail below, several different methods were usedto judge whether select biomarkers discriminate between the Sepsis andSIRS groups.

Wilcoxon and Q-Value Tests.

The first method used to identify discriminating biomarkers was aWilcoxon test (unadjusted). The abundance value for a given biomarkerfrom all samples in the training data was subjected to the Wilcoxontest. The Wilcoxon test considers both group classification (sepsisversus SIRS) and abundance value in order to compute a p value for thegiven biomarker. The p value provides an indication of how well theabundance value for the given biomarker across the samples collected inthe training set discriminates between the sepsis and SIRS state. Thelower the p value, the better the discrimination. When the p value isless than a specific confidence level, such as 0.05, an inference ismade that the biomarker discriminates between the sepsis and SIRSphenotype. There were 6427 significant biomarkers using this method (seeTable 23).

The second method used to identify discriminating biomarkers was theWilcoxon Test (adjusted). Due to the large number of biomarkers, 54613,and the relatively small number of samples, 89, there was a high risk offinding falsely significant biomarkers. An adjusted p-value was used tocounter this risk. In particular, the method of Benjamini and Hochberg,1995, J.R. Statist. Soc. B 57, pp 289-300, which is hereby incorporatedby reference in its entirety, was used to control the false discoveryrate. Here, the false discovery rate is defined as the number ofbiomarkers truly significant divided by the number of biomarkersdeclared significant. For example, if the adjusted p-value is less than0.05, there is a 5% chance that the biomarker is a false discovery.Results using this test are reported in Table 12. There were 482significant biomarkers using this method (see Table 23). As used,herein, a biomarker is considered significant if it has a p-value ofless than 0.05 as determined by the Wilcoxon test (adjusted).

The third method used to identify discriminating biomarkers was the useof Q values. The biomarkers are ordered by their q-values and if abiomarker has a q-value of X, then this biomarker and all others morebiomarkers have a combined false discovery rate of X. However, the falsediscovery rate for any one biomarker may be much larger. There were 482significant biomarkers using this method (see Table 23).

TABLE 23 Cumulative number of significant calls for the three methods.Note that all 89 samples (training and validation) were used to compareSepsis and SIRS groups. Missing biomarker values were not included inthe analyses. ≦1e−04 ≦0.001 ≦0.01 ≦0.025 ≦0.05 ≦0.1 ≦1 p-value 0 8082486 4230 6427 10051 54613 (un- adjusted) p-value 0 0 0 0 482 1035 54613(adjusted) q-value 0 0 0 0 606 1283 54613

CART.

In addition to analyzing the microarray data using Wilcoxon test andQ-value tests in order to identify biomarkers that discriminate betweenthe sepsis and SIRS subpopulations in the training set, classificationand regression tree (CART) analysis was used. CART is described inSection 5.5.1, above. Specifically, the data summarized above was usedto predict the disease state by iteratively partitioning the data basedon the best single-variable (biomarker) split of the data. In otherwords, at each stage of the tree building process, the biomarker whoseabundance value across the training population best discriminatesbetween the sepsis and SIRS population was invoked as a decision branch.Cross-validation was carried out, with the optimal number of splitsestimated independently in each of the 10 iterations. The final tree isdepicted in FIG. 21. In FIG. 21, decision 2102 makes a decision based onthe abundance of the biomarker that bind to U133 plus 2.0 probeX210119_at. If this biomarker that binds to X210119_at has an abundancethat is less than −0.03669 units in a biological sample from a subjectto be diagnosed (test biological sample), then control passes todecision 2104. If, on the other hand, the biomarker that binds toprobeset X210119_at has an abundance that is greater than −0.03669 unitsin the test biological sample, decision control passes to decision 2106.Decisions are made in this manner until a terminal leaf of the decisiontree is reached, at which point diagnoses of sepsis or SIRS is made. Thedecision tree in FIG. 21 makes use of the biomarkers that bind to thefollowing five U133 plus 2.0 oligonucleotides: X210119_at,X1552554_a_at, X1554390_s_at, X1552301_a_at, and X1555868_at.

FIG. 22 shows the distribution of the five biomarkers used in thedecision tree between the sepsis and SIRS groups in the training dataset. In FIG. 22, the top of each box denotes the 75^(th) percentile ofthe data across the training set and the bottom of each box denotes the25^(th) percentile, and the median value for each biomarker across thetraining set is drawn as a line within each box. In FIG. 22, biomarkersare labeled by the U133 plus 2.0 oligonucleotides to which they bind.The confusion matrix for the training data where the predictedclassifications were made from the cross-validated model is given inTable 24. From this confusion matrix, the overall accuracy was estimatedto be 80.9% with a 95% confidence interval of 69.5% to 89.4%. Theestimated sensitivity was 93.9% and the estimated specificity was 68.6%.

TABLE 24 Confusion matrix for training samples using the cross-validatedCART algorithm of FIG. 21. True Diagnosis Predicted Sepsis SIRS Sepsis31 11 SIRS 2 24

For the 21 validation samples held back from training data set, theoverall accuracy was estimated to be 71.4% with a 95% confidenceinterval of 47.8% to 88.7%, sensitivity 72.7% and specificity 70%. Table25 shows the confusion matrix for the validation samples.

TABLE 25 Confusion matrix for validation samples using thecross-validated CART algorithm of FIG. 21. True Diagnosis PredictedSepsis SIRS Sepsis 10 5 SIRS 1 5

Random Forests.

Another decision rule that can be developed using biomarkers is a RandomForests decision tree. To fit a random forest, the number of trees (e.g.bootstrap iterations) is specified. No more than 500 were used in thisexample, but at least 50 are needed for a burn-in period. The number oftrees was chosen based on the accuracy of the training data. For thisdata, 482 trees were used to train the algorithm (see FIG. 23). In FIG.23, curve 2302 is a smoothed estimate of overall accuracy as a functionof tree number. Curve 2304 is a smoothed curve of tree sensitivity as afunction of tree number. Curve 2306 is a smoothed curve of treespecificity as a function of tree number. Using this algorithm, 482biomarkers had non-zero importance and were used in the model. Therandom forest algorithm gauges biomarker importance by the averagereduction in the training accuracy. The biomarkers were ranked by thismethod and are shown in FIG. 24. The figure only reflects the 50 mostimportant biomarkers found by using Random Forest analysis. However, 893biomarkers were actually found to have discriminating significance. Therandom forest method uses a number of different decision trees. Abiomarker is considered to have discriminating significance if it servedas a decision branch of a decision tree from a significant random forestanalysis. As used herein, a significant random forest analysis is onewhere the lower 95% confidence interval on accuracy by cross validationon a training data set is greater than 50% and the point estimate foraccuracy on a validation set is greater than 65%.

The predicted confusion matrix for the training dataset using thedecision tree developed using the Random Forest method is given in Table26. From this confusion matrix, the overall accuracy was estimated to be61.8% (confidence intervals cannot be computed when using the bootstrapaccuracy estimate). The estimated sensitivity was 57.6% and theestimated specificity was 65.7%.

TABLE 26 Confusion matrix for training samples against the decision treedeveloped using the Random Forest method. True Diagnosis PredictedSepsis SIRS Sepsis 23 14 SIRS 12 19

For the 21 validation samples held back from training, the overallaccuracy was estimated to be 72.6% with a 95% confidence interval of52.8% to 91.8%, sensitivity 63.9% and specificity 90%. Table 27 showsthe confusion matrix for the validation samples.

TABLE 27 Confusion matrix for the 20 validation samples against thedecision tree developed using the Random Forest method. True DiagnosisPredicted Sepsis SIRS Sepsis 7 1 SIRS 4 9

PAM.

Yet another decision rule developed using biomarkers is predictiveanalysis of microarrays (PAM), which is described in Section 5.5.2,above. In this method, a shrinkage parameter that determines the numberof biomarkers used to classify samples is specified. This parameter waschosen via cross-validation. There were no biomarkers with missingvalues. Based on cross-validation, the optimal threshold value was 1.62,corresponding to 269 biomarkers. FIG. 25 shows the accuracy acrossdifferent thresholds. In FIG. 25, curve 2502 is the overall accuracy(with 95% confidence interval bars). Curve 2504 shows decision rulesensitivity as a function of threshold value. Curve 2506 shows decisionrule specificity as a function of threshold value. Using the thresholdof 1.62, the overall accuracy for the training samples was estimated tobe 67.7% with a 95% confidence interval of 55.9% to 77.6%. The estimatedsensitivity was 68.6% and the estimated specificity was 66.7%. Table 28shows the confusion matrix for the training data where the predictedclassifications were made from the cross-validated models.

TABLE 28 Confusion matrix for training samples using cross-validated PAMalgorithm True Diagnosis Predicted Sepsis SIRS Sepsis 24 11 SIRS 11 22

For the 21 validation samples held back from training, the overallaccuracy was estimated to be 81% with a 95% confidence interval of 58.1%to 94.6%, sensitivity 72.7% and specificity 100%. Table 26 shows theconfusion matrix for the validation samples.

TABLE 29 Confusion matrix for validation samples using cross-validatedPAM algorithm True Diagnosis Predicted Sepsis SIRS Sepsis 8 1 SIRS 3 9

FIG. 26 shows the selected biomarkers, ranked by their relativediscriminatory power, and their relative importance in the model. FIG.26 only shows the fifty most important biomarkers found using the PAManalysis. However, 269 biomarker were found. In FIG. 26, biomarkers arelabeled by the U133 plus 2.0 oligonucleotides to which they bind.

FIG. 27 provides a summary of the CART, PAM and random forestsclassification algorithm (decision rule) performance and associated 95%confidence intervals. Fifty distinct biomarkers were selected fromacross all the algorithms illustrated in FIG. 27. FIG. 28 illustratesthe number of times that common biomarkers were selected across thetechniques of CART, PAM, RF, and Wilcoxon (adjusted). In FIG. 28,biomarkers are labeled by the U133 plus 2.0 oligonucleotide to whichthey bind. FIG. 29 illustrates an overall ranking of biomarkers for theT₀ base data set. In FIG. 29, biomarkers are labeled by the U133 plus2.0 oligonucleotide to which they bind. For the selected biomarkers, thex-axis depicts the percentage of times that it was selected. Within thepercentage of times that biomarkers were selected, the biomarkers areranked.

6.6 Select Biomarkers

Sections 6.3 through 6.5 describe experiments in which blood samplesfrom SIRS positive subjects have been tested using Affymetrix U133 plus2.0 human genome chips containing 54,613 probesets. This sectiondescribes the criteria applied to the data described in Sections 6.3through 6.5 in order to identify a list of biomarkers that discriminatebetween subjects likely to develop sepsis in a defined time period(sepsis subjects) and subjects not likely to develop sepsis in a definedtime period (SIRS subjects). FIG. 30 illustrates the filters applied toidentify this list of biomarkers.

A first criterion that was imposed was a requirement that a biomarkerdiscriminate between SIRS and sepsis with a p value of 0.05 or less, asdetermined by the Wilcoxon test after correction for multiplecomparisons, at any time point measured or the biomarker was used in amultivariate analysis with significant classification performance wheresignificant classification performance is defined by having a lower95^(th) percentile for accuracy on a training data set that is greaterthan 50% and a point estimate for accuracy on the validation set greaterthan 65% at any time point measured. At T⁻³⁶ (Section 6.3), 1,618biomarkers met this criterion. At T⁻¹² (Section 6.4), 12,728 biomarkersmet this criterion. Some biomarkers met this criterion at both T⁻¹² andT⁻³⁶ time points. In total, there were 14,346 biomarkers (includingduplicates from T⁻¹² and T⁻³⁶ time points) that discriminated betweenthe sepsis and SIRS states. Thus, the first filter criterion reduced thenumber of eligible biomarkers from 54,613 to 14,346.

The second criterion that was imposed was a requirement that eachrespective biomarker under consideration exhibit at least a 1.2× foldchange between the median value for the respective biomarker among thesubjects that acquired sepsis during a defined time period (sepsissubjects) and the median value for the respective biomarker amongsubjects that do not acquire sepsis during the defined time period (SIRSsubjects) at the T⁻³⁶ time or the T⁻¹² time point period. Furthermore,to satisfy the second criterion, the biomarker must have been used in atleast one multivariate analysis with significant classificationperformance where significant classification performance is defined byhaving a lower 95^(th) percentile for accuracy on a training data setthat is greater than 50% and a point estimate for accuracy on thevalidation set that is greater then 65% at any time point measured. Asnoted in FIG. 30, application of the third filter criterion reduced thenumber of eligible biomarkers from 14,346 to 626.

In column one of Table 30, each biomarker is listed by a gene name, suchas, for example, a Human Gene Nomenclature Database (HUGO) symbol setforth by the Gene Nomenclature Committee, Department of Biology,University College London. As is known in the art, some human genomegenes are represented by more than one probeset in the U133 plus 2.0array. Furthermore, some of the oligonucleotides in the U133 plus 2.0array represent expressed sequence tags (ESTs) that do not correspond toa known gene (see column two of Table 30). Where known, the names of thedifferent human genes are listed in column three of Table 30.

In the case where a biomarker is based upon a gene that includes thesequence of a probeset listed in Table 30 or a complement thereof, thebiomarker can be, for example, a transcript made by the gene, acomplement thereof, or a discriminating fragment or complement thereof,or a cDNA thereof, or a discriminating fragment of the cDNA, or adiscriminating amplified nucleic acid molecule corresponding to all or aportion of the transcript or its complement, or a protein encoded by thegene, or a discriminating fragment of the protein, or an indication ofany of the above. Further still, the biomarker can be, for example, aprotein encoded by a gene that includes a probeset sequence described inTable 30 or a discriminating fragment of the protein, or an indicationof the above. Here, a discriminating molecule or fragment is a moleculeor fragment that, when detected, indicates presence or abundance of theabove-identified transcript, cDNA, amplified nucleic acid, or protein.In one embodiment, a biomarker profile of the present inventioncomprises a plurality of biomarkers that contain at least five, at leastten at least fifteen, at least twenty, at least thirty, between 2 and 5,between 3 and 7, or less than 15 of the sequences of the probesets ofTable 30, or complements thereof, or genes including one of at leastfive of the sequences or complements thereof, or a discriminatingfragment thereof, or an amino acid sequence encoded by any of theforegoing nucleic acid sequences, or any discriminating fragment of suchan amino acid sequence. Such biomarkers can be mRNA transcripts, cDNA orsome other form of amplified nucleic acid or proteins. In someembodiments a biomarker is any gene that includes the sequence in anAffymetrix probeset given in Table 30, or any gene that includes acomplement of the sequence in an Affymetrix probeset given in Table 30,or any mRNA, cDNA or other form of amplified nucleic acid of theforegoing, for any discriminating fragment of the foregoing, or anyamino acid sequence coded by the foregoing, or any discriminatingfragment of such a protein.

TABLE 30 Exemplary biomarkers that discriminate between responders andnonresponders Gene Protein Affymetrix Accession Accession Gene SymbolProbeset name Gene Name Number Number Column Column Column Column Column1 2 3 4 5 FLJ20445 218582_at HYPOTHETICAL PROTEIN NM_017824 FLJ204453′HEXO 231852_at HISTONE MRNA 3′ END NM_153332 NP_699163 EXORIBONUCLEASE3′HEXO 226416_at HISTONE MRNA 3′ END NM_153332 NP_699163 EXORIBONUCLEASEABCA2 212772_s_at ATP-BINDING CASSETTE, NM_001606 NP_001597 SUB-FAMILY A(ABC1), NM_212533 NP_997698 MEMBER 2 ABHD2 228490_at ABHYDROLASE DOMAINNM_007011 NP_008942 CONTAINING 2 NM_152924 NP_690888 ACN9 218981_at ACN9HOMOLOG (S. CEREVISIAE) NM_020186 NP_064571 ACSL1 201963_at ACYL-COASYNTHETASE NM_001995 NP_001986 LONG-CHAIN FAMILY MEMBER 1 ACSL3201660_at ACYL-COA SYNTHETASE NM_004457NM_203372 NP_004448 LONG-CHAINFAMILY NP_976251 MEMBER 3 ACSL4 202422_s_at ACYL-COA SYNTHETASENM_004458 NP_004449 LONG-CHAIN FAMILY NM_022977 NP_075266 MEMBER 4 ACTR3213101_s_at ARP3 ACTIN-RELATED NM_005721 NP_005712 PROTEIN 3 HOMOLOG(YEAST) ADM 202912_at ADRENOMEDULLIN NM_001124 NP_001115 ADORA2A205013_s_at ADENOSINE A2 NM_000675 NP_000666 RECEPTOR AIM2 206513_atABSENT IN MELANOMA 2 NM_004833 NP_004824 ALOX5AP 204174_at ARACHIDONATE5- NM_001629 NP_001620 LIPOXYGENASE- ACTIVATING PROTEIN AMPD2 212360_atADENOSINE NM_004037 NP_004028 MONOPHOSPHATE NM_139156 NP_631895DEAMINASE 2 (ISOFORM NM_203404 NP_981949 L) ANKRD22 238439_at ANKYRINREPEAT NM_144590 NP_653191 DOMAIN 22 ANKRD22 239196_at ANKYRIN REPEATNM_144590 NP_653191 DOMAIN 22 ANXA3 209369_at ANNEXIN A3 NM_005139NP_005130 APG3L 220237_at APG3 AUTOPHAGY 3-LIKE NM_022488 NP_071933 (S.CEREVISIAE) ARHGAP8 47069_at RHO GTPASE NM_015366 NP_056181 ACTIVATINGPROTEIN 8 NM_017701 NP_060171 NM_181333 NP_851850 NM_181334 NP_851851NM_181335 NP_851852 ARID5B 212614_at AT RICH INTERACTIVE XM_084482XP_084482 DOMAIN 5B (MRF1-LIKE) ASAHL 214765_s_at N-ACYLSPHINGOSINENM_014435 NP_055250 AMIDOHYDROLASE-LIKE PROTEIN ASAHL 232072_atN-ACYLSPHINGOSINE NM_014435 NP_055250 AMIDOHYDROLASE-LIKE PROTEIN ASAHL227135_at N-ACYLSPHINGOSINE NM_014435 NP_055250 AMIDOHYDROLASE-LIKEPROTEIN ASPH 242037_at ASPARTATE BETA- NM_004318 NP_004309 HYDROXYLASENM_020164 NP_064549 NM_032466 NP_115855 NM_032467 NP_115856 NM_032468NP_115857 ATP11B 1554557_at ATPASE, CLASS VI, TYPE XM_087254 XP_08725411B ATP11B 1564064_a_at ATPASE, CLASS VI, TYPE XM_087254 XP_087254 11BATP11B 1554556_a_at ATPASE, CLASS VI, TYPE XM_087254 XP_087254 11BATP11B 212536_at ATPASE, CLASS VI, TYPE XM_087254 XP_087254 11B ATP11B1564063_a_at ATPASE, CLASS VI, TYPE XM_087254 XP_087254 11B ATP6V1C1202872_at ATPASE, H+ NM_001007254 NP_001007255 TRANSPORTING, NM_001695NP_001686 LYSOSOMAL, 42-KD, V1 SUBUNIT C, ISOFORM 1 ATP6V1C1 202874_s_atATPASE, H+ NM_001007254 NP_001007255 TRANSPORTING, NM_001695 NP_001686LYSOSOMAL, 42-KD, V1 SUBUNIT C, ISOFORM 1 ATP6V1C1 226463_at ATPASE, H+NM_001007254 NP_001007255 TRANSPORTING, NM_001695 NP_001686 LYSOSOMAL,42-KD, V1 SUBUNIT C, ISOFORM 1 ATP9A 212062_at ATPASE, CLASS II, TYPEXM_030577 XP_030577 9A B4GALT5 221485_at BETA-1,4- NM_004776 NP_004767GALACTOSYLTRANSFERASE BASP1 202391_at BRAIN-ABUNDANT NM_006317 NP_006308SIGNAL PROTEIN BAT5 224756_s_at HLA-B ASSOCIATED NM_021160 NP_066983TRANSCRIPT 5 BATF 205965_at BASIC LEUCINE ZIPPER NM_006399 NP_006390TRANSCRIPTION FACTOR, ATF-LIKE BAZ1A 217986_s_at BROMODOMAIN NM_013448NP_038476 ADJACENT TO ZINC NM_182648 NP_872589 FINGER DOMAIN, 1A BAZ1A217985_s_at BROMODOMAIN NM_013448 NP_038476 ADJACENT TO ZINC NM_182648NP_872589 FINGER DOMAIN, 1A BCL2A1 205681_at BCL2-RELATED PROTEINNM_004049 NP_004040 A1 BCL3 204908_s_at B-CELL CLL/LYMPHOMA 3 NM_005178NP_005169 BCL3 204907_s_at B-CELL CLL/LYMPHOMA 3 NM_005178 NP_005169BCL6 203140_at B-CELL LYMPHOMA 6 NM_001706 NP_001697 NM_138931 NP_620309BCL6 215990_s_at B-CELL LYMPHOMA 6 NM_001706 NP_001697 NM_138931NP_620309 BIK 205780_at BCL2-INTERACTING NM_001197 NP_001188 KILLER(APOPTOSIS- INDUCING) BMX 206464_at BONE MARROW KINASE, NM_001721NP_001712 X-LINKED NM_203281 NP_975010 C13orf12 217769_s_at CHROMOSOME13 OPEN NM_015932 NP_057016 READING FRAME 12 C14orf101 225675_atCHROMOSOME 14 OPEN NM_017799 NP_060269 READING FRAME 101 C14orf101219757_s_at CHROMOSOME 14 OPEN NM_017799 NP_060269 READING FRAME 101C14orf147 213508_at CHROMOSOME 14 OPEN NM_138288 NP_612145 READING FRAME147 C16orf30 219315_s_at CHROMOSOME 16 OPEN NM_024600 NP_078876 READINGFRAME 30 C16orf7 205781_at CHROMOSOME 16 OPEN- NM_004913 NP_004904READING FRAME 7 C1GALT1 219439_at CORE 1 SYNTHASE, NM_020156 NP_064541GLYCOPROTEIN-N- ACETYLGALACTOSAMINE 3-BETA- GALACTOSYLTRANSFERASEC1GALT1C1 219283_at C1GALT1-SPECIFIC NM_001011551 NP_001011551 CHAPERONE1 NM_152692 NP_689905 C1GALT1C1 238989_at C1GALT1-SPECIFIC NM_001011551NP_001011551 CHAPERONE 1 NM_152692 NP_689905 C1orf8 200620_at CHROMOSOME1 OPEN NM_004872 NP_004863 READING FRAME 8 C1RL 218983_at COMPLEMENTNM_016546 NP_057630 COMPONENT 1, R SUBCOMPONENT-LIKE C20orf24217835_x_at CHROMOSOME 20 OPEN NM_018840 NP_061328 READING FRAME 24NM_199483 NP_955777 NM_199484 NP_955778 NM_199485 NP_955779 C20orf24223880_x_at CHROMOSOME 20 OPEN NM_018840 NP_061328 READING FRAME 24NM_199483 NP_955777 NM_199484 NP_955778 NM_199485 NP_955779 C20orf321554786_at CHROMOSOME 20 OPEN- NM_020356 NP_065089 READING FRAME 32C21orf91 220941_s_at CHROMOSOME 21 OPEN NM_017447 NP_059143 READINGFRAME 91 C2orf25 217883_at CHROMOSOME 2 OPEN NM_015702 NP_056517 READINGFRAME 25 C2orf33 223354_x_at CHROMOSOME 2 OPEN NM_020194 NP_064579READING FRAME 33 C6orf83 225850_at CHROMOSOME 6 OPEN NM_145169 NP_660152READING FRAME 83 C9orf19 225604_s_at CHROMOSOME 9 OPEN NM_022343NP_071738 READING FRAME 19 C9orf46 218992_at CHROMOSOME 9 OPEN NM_018465NP_060935 READING FRAME 46 C9orf84 1553920_at CHROMOSOME 9 OPENNM_173521 NP_775792 READING FRAME 84 CA4 206208_at CARBONIC ANHYDRASENM_000717 NP_000708 IV CA4 206209_s_at CARBONIC ANHYDRASE NM_000717NP_000708 IV CAB39 217873_at CALCIUM BINDING NM_016289 NP_057373 PROTEIN39 CACNA1E 236013_at CALCIUM CHANNEL, NM_000721 NP_000712VOLTAGE-DEPENDENT, ALPHA 1E SUBUNIT CACNA2D3 219714_s_at CALCIUMCHANNEL, NM_018398 NP_060868 VOLTAGE-DEPENDENT, ALPHA 2/DELTA 3 SUBUNITCAPZA2 1569450_at CAPPING PROTEIN (ACTIN NM_006136 NP_006127 FILAMENT)MUSCLE Z- LINE, ALPHA 2 CARD12 1552553_a_at CASPASE RECRUITMENTNM_021209 NP_067032 DOMAIN FAMILY, MEMBER 12 CASP4 209310_s_at CASPASE4, APOPTOSIS- NM_001225 NP_001216 RELATED CYSTEINE NM_033306 NP_150649PROTEASE NM_033307 NP_150650 CCL5 1555759_a_at CHEMOKINE (C-C MOTIF)NM_002985 NP_002976 LIGAND 5 CCPG1 221511_x_at CELL CYCLE NM_004748NP_004739 PROGRESSION 1 NM_020739 NP_065790 CD4 203547_at CD4 ANTIGEN(P55) NM_000616 NP_000607 CD48 237759_at CD48 ANTIGEN (B-CELL NM_001778NP_001769 MEMBRANE PROTEIN) CD58 211744_s_at CD58 ANTIGEN, NM_001779NP_001770 (LYMPHOCYTE FUNCTION-ASSOCIATED ANTIGEN 3) CD58 205173_x_atCD58 ANTIGEN, NM_001779 NP_001770 (LYMPHOCYTE FUNCTION-ASSOCIATEDANTIGEN 3) CD58 216942_s_at CD58 ANTIGEN, NM_001779 NP_001770(LYMPHOCYTE FUNCTION-ASSOCIATED ANTIGEN 3) CD59 228748_at CD59 ANTIGENP18-20 NM_000611 NP_000602 (ANTIGEN IDENTIFIED BY NM_203329 NP_976074MONOCLONAL NM_203330 NP_976075 ANTIBODIES 16.3A5, EJ16, NM_203331NP_976076 EJ30, EL32 AND G344) CD59 200985_s_at CD59 ANTIGEN P18-20NM_000611 NP_000602 (ANTIGEN IDENTIFIED BY NM_203329 NP_976074MONOCLONAL NM_203330 NP_976075 ANTIBODIES 16.3A5, EJ16, NM_203331NP_976076 EJ30, EL32 AND G344) CD59 200984_s_at CD59 ANTIGEN P18-20NM_000611 NP_000602 (ANTIGEN IDENTIFIED BY NM_203329 NP_976074MONOCLONAL NM_203330 NP_976075 ANTIBODIES 16.3A5, EJ16, NM_203331NP_976076 EJ30, EL32 AND G344) CD59 212463_at CD59 ANTIGEN P18-20NM_000611 NP_000602 (ANTIGEN IDENTIFIED BY NM_203329 NP_976074MONOCLONAL NM_203330 NP_976075 ANTIBODIES 16.3A5, EJ16, NM_203331NP_976076 EJ30, EL32 AND G344) CD74 209619_at CD74 ANTIGEN NM_004355NP_004346 (INVARIANT POLYPEPTIDE OF MAJOR HISTOCOMPATIBILITY COMPLEX,CLASS II ANTIGEN-ASSOCIATED) CD74 1567628_at CD74 ANTIGEN NM_004355NP_004346 (INVARIANT POLYPEPTIDE OF MAJOR HISTOCOMPATIBILITY COMPLEX,CLASS II ANTIGEN-ASSOCIATED) CD86 210895_s_at CD86 ANTIGEN (CD28NM_006889 NP_008820 ANTIGEN LIGAND 2, B7-2 NM_175862 NP_787058 ANTIGEN)CDKN3 209714_s_at CYCLIN-DEPENDENT NM_005192 NP_005183 KINASE INHIBITOR3 (CDK2-ASSOCIATED DUAL SPECIFICITY PHOSPHATASE) CEACAM1 209498_atCARCINOEMBRYONIC NM_001712 NP_001703 ANTIGEN-RELATED CELL ADHESIONMOLECULE 1 CEACAM1 206576_s_at CARCINOEMBRYONIC NM_001712 NP_001703ANTIGEN-RELATED CELL ADHESION MOLECULE 1 CEACAM1 211889_x_atCARCINOEMBRYONIC NM_001712 NP_001703 ANTIGEN-RELATED CELL ADHESIONMOLECULE 1 CEACAM1 211883_x_at CARCINOEMBRYONIC NM_001712 NP_001703ANTIGEN-RELATED CELL ADHESION MOLECULE 1 CEACAM3 208052_x_atCARCINOEMBRYONIC NM_001815 NP_001806 ANTIGEN-RELATED CELL ADHESIONMOLECULE 3 CECR1 219505_at CAT EYE SYNDROME NM_017424 NP_059120CHROMOSOME REGION, NM_177405 NP_803124 CANDIDATE 1 CHCHD7 222701_s_atCOILED-COIL-HELIX NM_001011667 NP_001011667 DOMAIN-CONTAININGNM_001011668 NP_001011668 PROTEIN 7 NM_001011669 NP_001011669NM_001011670 NP_001011670 NM_001011671 NP_001011671 NM_024300 NP_077276CHSY1 203044_at CARBOHYDRATE NM_014918 NP_055733 SYNTHASE 1 CIR209571_at CBF1 INTERACTING NM_004882 NP_004873 COREPRESSOR NM_199075NP_951057 CKLF 223451_s_at CHEMOKINE-LIKE NM_016326 NP_057410 FACTORNM_016951 NP_058647 NM_181640 NP_857591 NM_181641 NP_857592 CKLF219161_s_at CHEMOKINE-LIKE NM_016326 NP_057410 FACTOR NM_016951NP_058647 NM_181640 NP_857591 NM_181641 NP_857592 CKLF 221058_s_atCHEMOKINE-LIKE NM_016326 NP_057410 FACTOR NM_016951 NP_058647 NM_181640NP_857591 NM_181641 NP_857592 CKLFSF1 235286_at CHEMOKINE-LIKE NM_052999NP_443725 FACTOR SUPER FAMILY 1 NM_181268 NP_851785 NM_181269 NP_851786NM_181270 NP_851787 NM_181271 NP_851788 NM_181272 NP_851789 NM_181283NP_851800 NM_181285 NP_851802 NM_181286 NP_851803 NM_181287 NP_851804NM_181288 NP_851805 NM_181289 NP_851806 NM_181290 NP_851807 NM_181292NP_851809 NM_181293 NP_851810 NM_181294 NP_851811 NM_181295 NP_851812NM_181296 NP_851813 NM_181297 NP_851814 NM_181298 NP_851815 NM_181299NP_851816 NM_181300 NP_851817 NM_181301 NP_851818 CLEC10A 206682_atC-TYPE LECTIN DOMAIN NM_006344 NP_006335 FAMILY 10, MEMBER A NM_182906NP_878910 CLEC4D 1552772_at C-TYPE LECTIN DOMAIN NM_080387 NP_525126FAMILY 4, MEMBER D CLEC4E 219859_at C-TYPE LECTIN DOMAIN NM_014358NP_055173 FAMILY 4, MEMBER E CLEC5A 219890_at C-TYPE LECTIN DOMAINNM_013252 NP_037384 FAMILY 5, MEMBER A COL4A3BP 223465_at COLLAGEN, TYPEIV, NM_005713 NP_005704 ALPHA 3 (GOODPASTURE NM_031361 NP_112729ANTIGEN) BINDING PROTEIN COP1 1552701_a_at CARD ONLY PROTEIN NM_052889NP_443121 COX15 235204_at COX15 HOMOLOG, NM_004376 NP_004367 CYTOCHROMEC NM_078470 NP_510870 OXIDASE ASSEMBLY PROTEIN (YEAST) CPD 201941_atCARBOXYPEPTIDASE D NM_001304 NP_001295 CPD 201940_at CARBOXYPEPTIDASE DNM_001304 NP_001295 CPEB4 242384_at CYTOPLASMIC NM_030627 NP_085130POLYADENYLATION ELEMENT BINDING PROTEIN 4 CPEB4 224829_at CYTOPLASMICNM_030627 NP_085130 POLYADENYLATION ELEMENT BINDING PROTEIN 4 CPVL208146_s_at CARBOXYPEPTIDASE, NM_019029 NP_061902 VITELLOGENIC-LIKENM_031311 NP_112601 CR1 206244_at COMPLEMENT NM_000573 NP_000564COMPONENT (3B/4B) NM_000651 NP_000642 RECEPTOR 1, INCLUDING KNOPS BLOODGROUP SYSTEM CRTAP 1554464_a_at CARTILAGE-ASSOCIATED NM_006371 NP_006362PROTEIN CRTAP 1555889_a_at CARTILAGE-ASSOCIATED NM_006371 NP_006362PROTEIN CSF1R 203104_at COLONY STIMULATING NM_005211 NP_005202 FACTOR 1RECEPTOR, FORMERLY MCDONOUGH FELINE SARCOMA VIRAL (V-FMS) ONCOGENEHOMOLOG CTGLF1 221850_x_at CENTAURIN, GAMMA- NM_133446 NP_597703 LIKEFAMILY, MEMBER 1 CYP4F2 210452_x_at CYTOCHROME P450, NM_001082 NP_001073FAMILY 4, SUBFAMILY F, POLYPEPTIDE 2 DCP2 235258_at DCP2 DECAPPINGNM_152624 NP_689837 ENZYME HOMOLOG (S. CEREVISIAE) DDAH2 202262_x_atDIMETHYLARGININE NM_013974 NP_039268 DIMETHYLAMINOHYDROLASE 2 DDAH2215537_x_at DIMETHYLARGININE NM_013974 NP_039268 DIMETHYLAMINOHYDROLASE2 DDAH2 214909_s_at DIMETHYLARGININE NM_013974 NP_039268DIMETHYLAMINOHYDROLASE 2 DDX26 222239_s_at DEAD/H (Asp-Glu-Ala-NM_012141 NP_036273 Asp/His) BOX POLYPEPTIDE 26 DHRS9 223952_x_atMEMBRANE PROTEIN, NM_005771 NP_005762 PALMITOYLATED 3; MPP3 NM_199204NP_954674 DHRS9 224009_x_at MEMBRANE PROTEIN, NM_005771 NP_005762PALMITOYLATED 3; MPP3 NM_199204 NP_954674 DHRS9 219799_s_at MEMBRANEPROTEIN, NM_005771 NP_005762 PALMITOYLATED 3; MPP3 NM_199204 NP_954674DKFZP564B1023 228385_at KINESIN FAMILY XM_375825 XP_375825 MEMBER 14(KIF14) DKFZP566M1046 223637_s_at HYPOTHETICAL PROTEIN NM_032127NP_115503 DKFZP566M1046 DKFZp667F0711 1559756_at HYPOTHETICAL PROTEINXM_374767 XP_374767 DKFZp667F0711 DLGAP1 239421_at DISCS, LARGENM_001003809 NP_001003809 (DROSOPHILA) NM_004746 NP_004737HOMOLOG-ASSOCIATED PROTEIN 1 DNAJA1 200881_s_at DNAJ (HSP40) HOMOLOG,NM_001539 NP_001530 SUBFAMILY A, MEMBER 1 DR1 216652_s_at DOWN-REGULATOROF NM_001938 NP_001929 TRANSCRIPTION 1, TBP- BINDING (NEGATIVE COFACTOR2) E2F3 203693_s_at E2F TRANSCRIPTION NM_001949 NP_001940 FACTOR 3 EFHC1225656_at EF-HAND DOMAIN (C- NM_018100 NP_060570 TERMINAL) CONTAINING 1EGFL5 212831_at EGF-LIKE-DOMAIN, XM_376905 XP_376905 MULTIPLE 5 EIF4E201435_s_at EUKARYOTIC NM_001968 NP_001959 TRANSLATION INITIATION FACTOR4E EIF4E3 225941_at EUKARYOTIC NM_173359 NP_775495 TRANSLATIONINITIATION FACTOR 4E MEMBER 3 EIF4E3 225940_at EUKARYOTIC NM_173359NP_775495 TRANSLATION INITIATION FACTOR 4E MEMBER 3 EIF4E3 238461_atEUKARYOTIC NM_173359 NP_775495 TRANSLATION INITIATION FACTOR 4E MEMBER 3EIF4G3 201935_s_at EUKARYOTIC NM_003760 NP_003751 TRANSLATION INITIATIONFACTOR 4- GAMMA, 3 EIF4G3 201936_s_at EUKARYOTIC NM_003760 NP_003751TRANSLATION INITIATION FACTOR 4- GAMMA, 3 EIF4G3 243149_at EUKARYOTICNM_003760 NP_003751 TRANSLATION INITIATION FACTOR 4- GAMMA, 3 EMILIN2242288_s_at ELASTIN MICROFIBRIL NM_032048 NP_114437 INTERFACER 2 ETS2201328_at V-ETS NM_005239 NP_005230 ERYTHROBLASTOSIS VIRUS E26 ONCOGENEHOMOLOG 2 (AVIAN) EXOSC4 91684_g_at EXOSOME COMPONENT 4 NM_019037NP_061910 EXOSC4 218695_at EXOSOME COMPONENT 4 NM_019037 NP_061910EXOSC4 58696_at EXOSOME COMPONENT 4 NM_019037 NP_061910 FAD104 244022_atFIBRONECTIN TYPE III NM_022763 NP_073600 DOMAIN CONTAINING 3B (FNDC3B)FAM53C 218023_s_at FAMILY WITH SEQUENCE NM_016605 NP_057689 SIMILARITY53, MEMBER C FAS 204781_s_at FAS (TNF RECEPTOR NM_000043 NP_000034SUPERFAMILY, MEMBER NM_152871 NP_690610 6) NM_152872 NP_690611 NM_152873NP_690612 NM_152874 NP_690613 NM_152875 NP_690614 NM_152876 NP_690615NM_152877 NP_690616 FAS 204780_s_at FAS (TNF RECEPTOR NM_000043NP_000034 SUPERFAMILY, MEMBER NM_152871 NP_690610 6) NM_152872 NP_690611NM_152873 NP_690612 NM_152874 NP_690613 NM_152875 NP_690614 NM_152876NP_690615 NM_152877 NP_690616 FBXL13 1553798_a_at F-BOX AND LEUCINE-NM_145032 NP_659469 RICH REPEAT PROTEIN 13 FBXO9 1559094_at F-BOXPROTEIN 9 NM_012347 NP_036479 NM_033480 NP_258441 NM_033481 NP_258442FBXO9 1559096_x_at F-BOX PROTEIN 9 NM_012347 NP_036479 NM_033480NP_258441 NM_033481 NP_258442 FCAR 211307_s_at FC FRAGMENT OF IGA,NM_002000 NP_001991 RECEPTOR FOR NM_133269 NP_579803 NM_133271 NP_579805NM_133272 NP_579806 NM_133273 NP_579807 NM_133274 NP_579808 NM_133277NP_579811 NM_133278 NP_579812 NM_133279 NP_579813 NM_133280 NP_579814FCAR 211306_s_at FC FRAGMENT OF IGA, NM_002000 NP_001991 RECEPTOR FORNM_133269 NP_579803 NM_133271 NP_579805 NM_133272 NP_579806 NM_133273NP_579807 NM_133274 NP_579808 NM_133277 NP_579811 NM_133278 NP_579812NM_133279 NP_579813 NM_133280 NP_579814 FCAR 207674_at FC FRAGMENT OFIGA, NM_002000 NP_001991 RECEPTOR FOR NM_133269 NP_579803 NM_133271NP_579805 NM_133272 NP_579806 NM_133273 NP_579807 NM_133274 NP_579808NM_133277 NP_579811 NM_133278 NP_579812 NM_133279 NP_579813 NM_133280NP_579814 FCAR 211305_x_at FC FRAGMENT OF IGA, NM_002000 NP_001991RECEPTOR FOR NM_133269 NP_579803 NM_133271 NP_579805 NM_133272 NP_579806NM_133273 NP_579807 NM_133274 NP_579808 NM_133277 NP_579811 NM_133278NP_579812 NM_133279 NP_579813 NM_133280 NP_579814 FCGR1A 216950_s_at FCFRAGMENT OF IGG, NM_000566 NP_000557 HIGH AFFINITY IA FCGR1A 216951_atFC FRAGMENT OF IGG, NM_000566 NP_000557 HIGH AFFINITY IA FCGR1A214511_x_at LOC440607 FEM1C 213341_at FEM-1 HOMOLOG C NM_020177NP_064562 (C. ELEGANS) FLJ10213 219906_at HYPOTHETICAL PROTEIN NM_018029NP_060499 FLJ10213 FLJ10521 1570511_at HYPOTHETICAL PROTEIN NM_001011722NP_001011722 FLJ10521 NM_018125 NP_060595 FLJ11011 222657_s_atHYPOTHETICAL PROTEIN NM_001001481 NP_001001481 FLJ11011 NM_001001482NP_001001482 NM_018299 NP_060769 FLJ11259 218627_at HYPOTHETICAL PROTEINNM_018370 NP_060840 FLJ11259 FLJ11795 220112_at HYPOTHETICAL PROTEINNM_024669 NP_078945 FLJ11795 FLJ12770 226059_at HYPOTHETICAL PROTEINNM_032174 NP_115550 FLJ12770 FLJ13154 218060_s_at HYPOTHETICAL PROTEINNM_024598 NP_078874 FLJ13154 FLJ13448 219397_at HYPOTHETICAL PROTEINNM_025147 NP_079423 FLJ13448 FLJ14001 238983_at HYPOTHETICAL PROTEINNM_024677 NP_078953 FLJ14001 FLJ20273 218035_s_at RNA-BINDING PROTEINNM_019027 NP_061900 FLJ20273 222496_s_at RNA-BINDING PROTEIN NM_019027NP_061900 FLJ20481 227889_at HYPOTHETICAL PROTEIN NM_017839 NP_060309FLJ20481 FLJ20481 222833_at HYPOTHETICAL PROTEIN NM_017839 NP_060309FLJ20481 FLJ20701 219093_at HYPOTHETICAL PROTEIN NM_017933 NP_060403FLJ20701 FLJ22833 219334_s_at HYPOTHETICAL PROTEIN NM_022837 NP_073748FLJ22833 FLJ22833 233085_s_at HYPOTHETICAL PROTEIN NM_022837 NP_073748FLJ22833 FLJ22833 222872_x_at HYPOTHETICAL PROTEIN NM_022837 NP_073748FLJ22833 FLJ23231 218810_at MCP-1 TREATMENT- NM_025079 NP_079355 INDUCEDPROTEIN (MCPIP) FLJ25416 228281_at HYPOTHETICAL PROTEIN NM_145018NP_659455 FLJ25416 FLJ31033 228152_s_at HYPOTHETICAL PROTEIN XM_037817XP_037817 FLJ31033 XM_376353 XP_376353 FLJ36031 226756_at HYPOTHETICALPROTEIN NM_175884 NP_787080 FLJ36031 FLJ37858 227354_at FLJ37858 PROTEINNM_001007549 NP_001007550 FLOT1 210142_x_at FLOTILLIN 1 NM_005803NP_005794 FNDC3B 225032_at FIBRONECTIN TYPE III NM_022763 NP_073600DOMAIN CONTAINING 3B FNDC3B 222692_s_at FIBRONECTIN TYPE III NM_022763NP_073600 DOMAIN CONTAINING 3B FNDC3B 222693_at FIBRONECTIN TYPE IIINM_022763 NP_073600 DOMAIN CONTAINING 3B FNDC3B 229865_at FIBRONECTINTYPE III NM_022763 NP_073600 DOMAIN CONTAINING 3B FNDC3B 218618_s_atFIBRONECTIN TYPE III NM_022763 NP_073600 DOMAIN CONTAINING 3B FTS218373_at FUSED TOES HOMOLOG NM_001012398 NP_001012398 (MOUSE) NM_022476NP_071921 FYB 227266_s_at FYN BINDING PROTEIN NM_001465 NP_001456(FYB-120/130) NM_199335 NP_955367 FYB 211795_s_at FYN BINDING PROTEINNM_001465 NP_001456 (FYB-120/130) NM_199335 NP_955367 G0S2 213524_s_atPUTATIVE LYMPHOCYTE NM_015714 NP_056529 G0/G1 SWITCH GENE GAB2 238405_atGRB2-ASSOCIATED NM_012296 NP_036428 BINDING PROTEIN 2 NM_080491NP_536739 GADD45A 203725_at GROWTH ARREST AND NM_001924 NP_001915DNA-DAMAGE- INDUCIBLE, ALPHA GADD45B 209304_x_at GROWTH ARREST- ANDNM_015675 NP_056490 DNA DAMAGE- INDUCIBLE GENE GADD45 GADD45B207574_s_at GROWTH ARREST- AND NM_015675 NP_056490 DNA DAMAGE- INDUCIBLEGENE GADD45 GALNT3 203397_s_at UDP-N-ACETYL-ALPHA-D- NM_004482 NP_004473GALACTOSAMINE:POLYPEPTIDE N- ACETYLGALACTOSAMINYL TRANSFERASE 3(GALNAC-T3) GBA 210589_s_at GLUCOSIDASE, BETA; NM_000157 NP_000148 ACID(INCLUDES NM_001005741 NP_001005741 GLUCOSYLCERAMIDASE) NM_001005742NP_001005742 NM_001005749 NP_001005749 NM_001005750 NP_001005750 GBA209093_s_at GLUCOSIDASE, BETA; NM_000157 NP_000148 ACID (INCLUDESNM_001005741 NP_001005741 GLUCOSYLCERAMIDASE) NM_001005742 NP_001005742NM_001005749 NP_001005749 NM_001005750 NP_001005750 GBP2 242907_atGUANYLATE BINDING NM_004120 NP_004111 PROTEIN 2, INTERFERON- INDUCIBLEGCA 203765_at GRANCALCIN, EF-HAND NM_012198 NP_036330 CALCIUM BINDINGPROTEIN GCLM 203925_at GLUTAMATE-CYSTEINE NM_002061 NP_002052 LIGASE,MODIFIER SUBUNIT GK 214681_at GLYCEROL KINASE NM_000167 NP_000158NM_203391 NP_976325 GK 207387_s_at GLYCEROL KINASE NM_000167 NP_000158NM_203391 NP_976325 GK 217167_x_at GLYCEROL KINASE NM_000167 NP_000158NM_203391 NP_976325 GK 216316_x_at GLYCEROL KINASE NM_000167 NP_000158NM_203391 NP_976325 GK 215977_x_at GLYCEROL KINASE NM_000167 NP_000158NM_203391 NP_976325 GNAI3 201180_s_at GUANINE NUCLEOTIDE NM_006496NP_006487 BINDING PROTEIN (G PROTEIN), ALPHA INHIBITING ACTIVITYPOLYPEPTIDE 3 GNG5 207157_s_at GUANINE NUCLEOTIDE NM_005274 NP_005265BINDING PROTEIN (G PROTEIN), GAMMA 5 GNS 212335_at GLUCOSAMINE (N-NM_002076 NP_002067 ACETYL)-6-SULFATASE (SANFILIPPO DISEASE IIID) GPR160223423_at G PROTEIN-COUPLED NM_014373 NP_055188 RECEPTOR 160 GPR43221345_at G PROTEIN-COUPLED NM_005306 NP_005297 RECEPTOR 43 GPR84223767_at G PROTEIN-COUPLED NM_020370 NP_065103 RECEPTOR 84 GPR97220404_at G PROTEIN-COUPLED NM_170776 NP_740746 RECEPTOR 97 GTDC1219770_at GLYCOSYLTRANSFERASE- NM_001006636 NP_001006637 LIKE DOMAINNM_024659 NP_078935 CONTAINING 1 GTF2B 208066_s_at GENERAL NM_001514NP_001505 TRANSCRIPTION FACTOR IIB GYG 201554_x_at GLYCOGENIN NM_004130NP_004121 GYG 211275_s_at GLYCOGENIN NM_004130 NP_004121 HAGH205012_s_at HYDROXYACYLGLUTATHIONE NM_005326 NP_005317 HYDROLASE HDAC4204225_at HISTONE DEACETYLASE 4 NM_006037 NP_006028 HGF 209960_atHEPATOCYTE GROWTH NM_000601 NP_000592 FACTOR (HEPAPOIETIN A;NM_001010931 NP_001010931 SCATTER FACTOR) NM_001010932 NP_001010932NM_001010933 NP_001010933 NM_001010934 NP_001010934 HIP1 226364_atHUNTINGTIN NM_005338 NP_005329 INTERACTING PROTEIN 1 HIP1 205425_atHUNTINGTIN NM_005338 NP_005329 INTERACTING PROTEIN 1 HIP1 205426_s_atHUNTINGTIN NM_005338 NP_005329 INTERACTING PROTEIN 1 HIST1H2BD209911_x_at HISTONE 1, H2BD NM_021063 NP_066407 NM_138720 NP_619790HIST2H2AA 214290_s_at HISTONE 2, H2AA NM_003516 NP_003507 HLA-DMA217478_s_at HLA-D NM_006120 NP_006111 HISTOCOMPATIBILITY TYPE HLA-DMB203932_at MAJOR NM_002118 NP_002109 HISTOCOMPATIBILITY COMPLEX, CLASSII, DM BETA HLA-DPA1 211990_at MAJOR NM_033554 NP_291032HISTOCOMPATIBILITY COMPLEX, CLASS II, DP ALPHA 1 HLA-DPA1 211991_s_atMAJOR NM_033554 NP_291032 HISTOCOMPATIBILITY COMPLEX, CLASS II, DP ALPHA1 HLA-DPB1 201137_s_at MAJOR NM_002121 NP_002112 HISTOCOMPATIBILITYCOMPLEX, CLASS II, DP BETA 1 HLA-DQB1 209823_x_at MAJOR NM_002123NP_002114 HISTOCOMPATIBILITY COMPLEX, CLASS II, DQ BETA 1 HLA-DQB1211656_x_at MAJOR NM_002123 NP_002114 HISTOCOMPATIBILITY COMPLEX, CLASSII, DQ BETA 1 HLA-DRA 208894_at MAJOR NM_002123 NP_002114HISTOCOMPATIBILITY COMPLEX, CLASS II, DR ALPHA HLA-DRA 210982_s_at MAJORNM_002123 NP_002114 HISTOCOMPATIBILITY COMPLEX, CLASS II, DR ALPHAHLA-DRB1 208306_x_at MAJOR NM_002124 NP_002115 HISTOCOMPATIBILITYCOMPLEX, CLASS II, DR BETA 1 HLA-DRB1 204670_x_at MAJOR NM_002124NP_002115 HISTOCOMPATIBILITY COMPLEX, CLASS II, DR BETA 1 HLA-DRB1209312_x_at MAJOR NM_002124 NP_002115 HISTOCOMPATIBILITY COMPLEX, CLASSII, DR BETA 1 HLA-DRB1 215193_x_at MAJOR NM_002124 NP_002115HISTOCOMPATIBILITY COMPLEX, CLASS II, DR BETA 1 HNRPLL 241692_atHETEROGENEOUS NM_138394 NP_612403 NUCLEAR RIBONUCLEOPROTEIN L- LIKE HPGD203913_s_at HYDROXYPROSTAGLANDIN NM_000860 NP_000851 DEHYDROGENASE 15-(NAD) HRPT2 218578_at HYPERPARATHYROIDISM NM_024529 NP_078805 2 (WITHJAW TUMOR) HSPC163 228306_at HSPC163 PROTEIN NM_014184 NP_054903 HSPC163218728_s_at HSPC163 PROTEIN NM_014184 NP_054903 HSPC163 228437_atHSPC163 PROTEIN NM_014184 NP_054903 HSPC163 223993_s_at HSPC163 PROTEINNM_014184 NP_054903 HSPC163 243051_at HSPC163 PROTEIN NM_014184NP_054903 HSPCA 214328_s_at HEAT SHOCK 90 KDA NM_005348 NP_005339PROTEIN 1, ALPHA HTATIP2 209448_at HIV-1 TAT INTERACTIVE NM_006410NP_006401 PROTEIN 2, 30 KDA HTATIP2 210253_at HIV-1 TAT INTERACTIVENM_006410 NP_006401 PROTEIN 2, 30 KDA IDI1 204615_x_at ISOPENTENYL-NM_004508 NP_004499 DIPHOSPHATE DELTA ISOMERASE IDI1 208881_x_atISOPENTENYL- NM_004508 NP_004499 DIPHOSPHATE DELTA ISOMERASE IFNAR1225669_at INTERFERON (ALPHA, NM_000629 NP_000620 BETA AND OMEGA)RECEPTOR 1 IFNAR1 225661_at INTERFERON (ALPHA, NM_000629 NP_000620 BETAAND OMEGA) RECEPTOR 1 IFNAR2 204786_s_at INTERFERON (ALPHA, NM_000874NP_000865 BETA AND OMEGA) NM_207584 NP_997467 RECEPTOR 2 NM_207585NP_997468 IFNGR1 202727_s_at INTERFERON GAMMA NM_000416 NP_000407RECEPTOR 1 IGSF2 207167_at IMMUNOGLOBULIN NM_004258 NP_004249SUPERFAMILY, MEMBER 2 IL10RA 204912_at INTERLEUKIN 10 NM_001558NP_001549 RECEPTOR, ALPHA IL18R1 206618_at INTERLEUKIN 18 NM_003855NP_003846 RECEPTOR 1 IL1R1 202948_at INTERLEUKIN 1 NM_000877 NP_000868RECEPTOR, TYPE I IL1R2 211372_s_at INTERLEUKIN 1 NM_004633 NP_004624RECEPTOR, TYPE II NM_173343 NP_775465 IL1R2 205403_at INTERLEUKIN 1NM_004633 NP_004624 RECEPTOR, TYPE II NM_173343 NP_775465 IL1RAP205227_at INTERLEUKIN 1 NM_002182 NP_002173 RECEPTOR ACCESSORY NM_134470NP_608273 PROTEIN INSL3 214572_s_at INSULIN-LIKE 3 (LEYDIG NM_005543NP_005534 CELL) IRAK2 231779_at INTERLEUKIN-1 NM_001570 NP_001561RECEPTOR-ASSOCIATED KINASE 2 IRAK3 220034_at INTERLEUKIN-1 NM_007199NP_009130 RECEPTOR-ASSOCIATED KINASE 3 IRAK4 219618_at INTERLEUKIN-1NM_016123 NP_057207 RECEPTOR-ASSOCIATED KINASE 4 ITGAM 205786_s_atINTEGRIN, ALPHA M NM_000632 NP_000623 (COMPLEMENT COMPONENT RECEPTOR 3,ALPHA; ALSO KNOWN AS CD11B (P170), MACROPHAGE ANTIGEN ALPHA POLYPEPTIDE)ITGB3 216261_at INTEGRIN, BETA 3 NM_000212 NP_000203 (PLATELETGLYCOPROTEIN IIIA, ANTIGEN CD61) IVNS1ABP 201362_at INFLUENZA VIRUS NS1ANM_006469 NP_006460 BINDING PROTEIN NM_016389 NP_057473 JAK2 205842_s_atJANUS KINASE 2 (A NM_004972 NP_004963 PROTEIN TYROSINE KINASE) JAK2205841_at JANUS KINASE 2 (A NM_004972 NP_004963 PROTEIN TYROSINE KINASE)JAK3 211108_s_at JANUS KINASE 3 (A NM_000215 NP_000206 PROTEIN TYROSINEKINASE, LEUKOCYTE) JAK3 227677_at JANUS KINASE 3 (A NM_000215 NP_000206PROTEIN TYROSINE KINASE, LEUKOCYTE) JUNB 201473_at JUN B PROTO-ONCOGENENM_002229 NP_002220 KCNE1 236407_at POTASSIUM VOLTAGE- NM_000219NP_000210 GATED CHANNEL, ISK- RELATED FAMILY, MEMBER 1 KCNJ15 238428_atPOTASSIUM INWARDLY- NM_002243 NP_002234 RECTIFYING CHANNEL, NM_170736NP_733932 SUBFAMILY J, MEMBER NM_170737 NP_733933 15 KCNJ15 210119_atPOTASSIUM INWARDLY- NM_002243 NP_002234 RECTIFYING CHANNEL, NM_170736NP_733932 SUBFAMILY J, MEMBER NM_170737 NP_733933 15 KIAA0040203144_s_at KIAA0040 NM_014656 NP_055471 KIAA0103 203584_at KIAA0103NM_014673 NP_055488 KIAA0182 212057_at KIAA0182 PROTEIN NM_014615NP_055430 KIAA0261 212264_s_at KIAA0261 NM_015045 NP_055860 KIAA0635206003_at CENTROSOMAL PROTEIN 4 NM_025009 NP_079285 KIAA0746 212314_atKIAA0746 PROTEIN NM_015187 NP_056002 KIAA1160 223831_x_at KIAA1160PROTEIN NM_020701 NP_065752 KIAA1533 244808_at KIAA1533 NM_020895NP_065946 KIAA1533 224807_at KIAA1533 NM_020895 NP_065946 KIAA1600226155_at KIAA1600 NM_020940 NP_065991 KIAA1632 227638_at KIAA1632NM_020964 NP_066015 KIAA1991 242808_at HYPOTHETICAL PROTEIN XM_495886XP_495886 KIAA1991 KIF1B 225878_at KINESIN FAMILY NM_015074 NP_055889MEMBER 1B NM_183416 NP_904325 KIF1B 209234_at KINESIN FAMILY NM_015074NP_055889 MEMBER 1B NM_183416 NP_904325 KIF1B 241216_at KINESIN FAMILYNM_015074 NP_055889 MEMBER 1B NM_183416 NP_904325 KLF11 218486_atKRUPPEL-LIKE FACTOR NM_003597 NP_003588 11 KLF7 204334_at KRUPPEL-LIKEFACTOR 7 NM_003709 NP_003700 (UBIQUITOUS) KLHL2 219157_at KELCH-LIKE 2,MAYVEN NM_007246 NP_009177 (DROSOPHILA) KLHL6 1560396_at KELCH-LIKE 6NM_130446 NP_569713 (DROSOPHILA) KPNA4 225267_at KARYOPHERIN ALPHA 4NM_002268 NP_002259 (IMPORTIN ALPHA 3) KPNA4 209653_at KARYOPHERIN ALPHA4 NM_002268 NP_002259 (IMPORTIN ALPHA 3) KREMEN1 227250_at KRINGLECONTAINING NM_032045 NP_114434 TRANSMEMBRANE NM_153379 NP_700358 PROTEIN1 KREMEN1 235370_at KRINGLE CONTAINING NM_032045 NP_114434 TRANSMEMBRANENM_153379 NP_700358 PROTEIN 1 KREMEN1 224534_at KRINGLE CONTAININGNM_032045 NP_114434 TRANSMEMBRANE NM_153379 NP_700358 PROTEIN 1 LDLR202068_s_at LOW DENSITY NM_000527 NP_000518 LIPOPROTEIN RECEPTOR LFNG228762_at LUNATIC FRINGE NM_002304 NP_002295 HOMOLOG (DROSOPHILA) LGALS8208934_s_at LECTIN, GALACTOSIDE- NM_006499 NP_006490 BINDING, SOLUBLE, 8NM_201543 NP_963837 (GALECTIN 8) NM_201544 NP_963838 NM_201545 NP_963839LIMK2 1561654_at LIM DOMAIN KINASE 2 NM_005569 NP_005560 NM_016733NP_057952 LIMK2 202193_at LIM DOMAIN KINASE 2 NM_005569 NP_005560NM_016733 NP_057952 LIMK2 210582_s_at LIM DOMAIN KINASE 2 NM_005569NP_005560 NM_016733 NP_057952 LIMK2 217475_s_at LIM DOMAIN KINASE 2NM_005569 NP_005560 NM_016733 NP_057952 LIN7A 240027_at LIN-7 HOMOLOG A(C. ELEGANS) NM_004664 NP_004655 LIN7A 206440_at LIN-7 HOMOLOG A (C.ELEGANS) NM_004664 NP_004655 LIR9 1555634_a_at LEUKOCYTE NM_021250NP_067073 IMMUNOGLOBULIN-LIKE NM_181879 NP_870994 RECEPTOR, SUBFAMILY BNM_181985 NP_871714 (WITH TM AND ITIM NM_181986 NP_871715 DOMAINS),MEMBER 7 (LILRB7) LMNB1 203276_at LAMIN B1 NM_005573 NP_005564 LMTK2226375_at LEMUR TYROSINE NM_014916 NP_055731 KINASE 2 LOC145758226513_at HYPOTHETICAL PROTEIN LOC145758 LOC153561 232889_atHYPOTHETICAL PROTEIN NM_207331 NP_997214 LOC153561 LOC199675 235568_atHYPOTHETICAL PROTEIN NM_174918 NP_777578 LOC199675 LOC220929 229743_atHYPOTHETICAL PROTEIN NM_182755 NP_877432 LOC220929 LOC285771 237870_atHYPOTHETICAL PROTEIN LOC285771 LOC286044 222662_at HYPOTHETICAL PROTEINLOC286044 LOC338758 238893_at HYPOTHETICAL PROTEIN LOC338758 LOC401152224602_at HCV F-TRANSACTIVATED NM_001001701 NP_001001701 PROTEIN 1LOC440731 237563_s_at LOC440731 XM_498838 XP_498838 LOC440823 227168_atLOC57149 203897_at HYPOTHETICAL PROTEIN NM_020424 NP_065157 A-211C6.1LOC88523 214748_at CG016 NM_033111 NP_149102 LRG1 228648_at LEUCINE-RICHALPHA-2- NM_052972 NP_443204 GLYCOPROTEIN 1 LRPAP1 201186_at LOW DENSITYNM_002337 NP_002328 LIPOPROTEIN RECEPTOR- RELATED PROTEIN ASSOCIATEDPROTEIN 1 LRRC17 1560527_at LEUCINE RICH REPEAT NM_005824 NP_005815CONTAINING 17 LTB4R 236172_at LEUKOTRIENE B4 NM_181657 NP_858043RECEPTOR LTBP2 204682_at LATENT TRANSFORMING NM_000428 NP_000419 GROWTHFACTOR BETA BINDING PROTEIN 2 LY86 205859_at LYMPHOCYTE ANTIGENNM_004271 NP_004262 86 LY96 206584_at LYMPHOCYTE ANTIGEN NM_015364NP_056179 96 MAP2K1IP1 217971_at MITOGEN-ACTIVATED NM_021970 NP_068805PROTEIN KINASE KINASE 1 INTERACTING PROTEIN 1 MAP2K6 205698_s_atMITOGEN-ACTIVATED NM_002758 NP_002749 PROTEIN KINASE KINASE 6 NM_031988NP_114365 MAPK14 210449_x_at MAPK14 MITOGEN- NM_001315 NP_001306ACTIVATED PROTEIN NM_139012 NP_620581 KINASE 14 NM_139013 NP_620582NM_139014 NP_620583 MAPK14 211561_x_at MAPK14 MITOGEN- NM_001315NP_001306 ACTIVATED PROTEIN NM_139012 NP_620581 KINASE 14 NM_139013NP_620582 NM_139014 NP_620583 MAPK14 211087_x_at MAPK14 MITOGEN-NM_001315 NP_001306 ACTIVATED PROTEIN NM_139012 NP_620581 KINASE 14NM_139013 NP_620582 NM_139014 NP_620583 MAPK14 202530_at MAPK14 MITOGEN-NM_001315 NP_001306 ACTIVATED PROTEIN NM_139012 NP_620581 KINASE 14NM_139013 NP_620582 NM_139014 NP_620583 MARCKSL1 200644_at MARCKS-LIKE 1NM_023009 NP_075385 MCTP2 220603_s_at MULTIPLE C2-DOMAINS NM_018349NP_060819 WITH TWO TRANSMEMBRANE REGIONS 2 MCTP2 229005_at MULTIPLEC2-DOMAINS NM_018349 NP_060819 WITH TWO TRANSMEMBRANE REGIONS 2 MCTP2239893_at MULTIPLE C2-DOMAINS NM_018349 NP_060819 WITH TWO TRANSMEMBRANEREGIONS 2 MEF2A 214684_at MADS BOX NM_005587 NP_005578 TRANSCRIPTIONENHANCER FACTOR 2, POLYPEPTIDE A (MYOCYTE ENHANCER FACTOR 2A) MEF2C236395_at MADS BOX NM_002397 NP_002388 TRANSCRIPTION ENHANCER FACTOR 2,POLYPEPTIDE C (MYOCYTE ENHANCER FACTOR 2C) MGC11324 224480_s_atHYPOTHETICAL PROTEIN NM_032717 NP_116106 MGC11324 MGC15619 226879_atHYPOTHETICAL PROTEIN NM_032369 NP_115745 MGC15619 MGC15887 226448_atHYPOTHETICAL GENE NM_198552 NP_940954 SUPPORTED BY BC009447 MGC17301227055_at HYPOTHETICAL PROTEIN NM_152637 NP_689850 MGC17301 MGC23280226121_at HYPOTHETICAL PROTEIN NM_144683 NP_653284 MGC23280 MKNK1209467_s_at MAP KINASE NM_003684 NP_003675 INTERACTING NM_198973NP_945324 SERINE/THREONINE KINASE 1 MLKL 238025_at MIXED LINEAGE KINASENM_152649 NP_689862 DOMAIN-LIKE MLLT2 201924_at MYELOID/LYMPHOID ORNM_005935 NP_005926 MIXED-LINEAGE LEUKEMIA (TRITHORAX HOMOLOG,DROSOPHILA); TRANSLOCATED TO, 2 MMP9 203936_s_at MATRIX NM_004994NP_004985 METALLOPROTEINASE 9 (GELATINASE B, 92 KDA GELATINASE, 92 KDATYPE IV COLLAGENASE) MOBK1B 201298_s_at MOB1, MPS ONE BINDER NM_018221NP_060691 KINASE ACTIVATOR-LIKE 1B (YEAST) MOBKL2C 227066_at MOB1, MPSONE BINDER NM_145279 NP_660322 KINASE ACTIVATOR-LIKE NM_201403 NP_9588052C (YEAST) MPEG1 226818_at MACROPHAGE XM_166227 XP_166227 EXPRESSED GENE1 MPEG1 226841_at MACROPHAGE XM_166227 XP_166227 EXPRESSED GENE 1 MSL3L1207551_s_at MALE-SPECIFIC LETHAL NM_006800 NP_006791 3-LIKE 1(DROSOPHILA) NM_078628 NP_523352 NM_078629 NP_523353 NM_078630 NP_523354MSL3L1 214009_at MALE-SPECIFIC LETHAL NM_006800 NP_006791 3-LIKE 1(DROSOPHILA) NM_078628 NP_523352 NM_078629 NP_523353 NM_078630 NP_523354MSRB2 218773_s_at METHIONINE SULFOXIDE NM_012228 NP_036360 REDUCTASE B2MTF1 227150_at METAL-REGULATORY NM_005955 NP_005946 TRANSCRIPTION FACTOR1 MTMR6 214429_at MYOTUBULARIN NM_004685 NP_004676 RELATED PROTEIN 6MYO10 201976_s_at MYOSIN X NM_012334 NP_036466 NALP1 210113_s_at NACHT,LEUCINE RICH NM_014922 NP_055737 REPEAT AND PYD (PYRIN NM_033004NP_127497 DOMAIN) CONTAINING 1 NM_033006 NP_127499 NM_033007 NP_127500NALP1 211824_x_at NACHT, LEUCINE RICH NM_014922 NP_055737 REPEAT AND PYD(PYRIN NM_033004 NP_127497 DOMAIN) CONTAINING 1 NM_033006 NP_127499NM_033007 NP_127500 NARF 219862_s_at NUCLEAR PRELAMIN A NM_012336NP_036468 RECOGNITION FACTOR NM_031968 NP_114174 NBS1 202906_s_atNIJMEGEN BREAKAGE NM_002485 NP_002476 SYNDROME 1 (NIBRIN) NBS1217299_s_at NIJMEGEN BREAKAGE NM_002485 NP_002476 SYNDROME 1 (NIBRIN)NCR1 207860_at NATURAL NM_004829 NP_004820 CYTOTOXICITY TRIGGERINGRECEPTOR 1 NDST2 203916_at N-DEACETYLASE/N- NM_003635 NP_003626SULFOTRANSFERASE (HEPARAN GLUCOSAMINYL) 2 NDUFA1 202298_at NADHDEHYDROGENASE NM_004541 NP_004532 (UBIQUINONE) 1 ALPHA SUBCOMPLEX, 1,7.5 KDA NDUFB3 203371_s_at NADH DEHYDROGENASE NM_002491 NP_002482(UBIQUINONE) 1 BETA SUBCOMPLEX, 3, 12 KDA NFKBIZ 223218_s_at NUCLEARFACTOR OF NM_001005474 NP_001005474 KAPPA LIGHT NM_031419 NP_113607POLYPEPTIDE GENE ENHANCER IN B-CELLS INHIBITOR, ZETA NMI 203964_at N-MYC(AND STAT) NM_004688 NP_004679 INTERACTOR NT5C2 209155_s_at5′-NUCLEOTIDASE, NM_012229 NP_036361 CYTOSOLIC II NTNG2 233072_at NETRING2 NM_032536 NP_115925 NUPL1 204435_at NUCLEOPORIN LIKE 1 NM_001008564NP_001008564 NM_001008565 NP_001008565 NM_014089 NP_054808 OACT2226726_at O-ACYLTRANSFERASE NM_138799 NP_620154 (MEMBRANE BOUND) DOMAINCONTAINING 2 OAT 201599_at ORNITHINE NM_000274 NP_000265AMINOTRANSFERASE (GYRATE ATROPHY) OMG 207093_s_at OLIGODENDROCYTENM_002544 NP_002535 MYELIN GLYCOPROTEIN OPLAH 222025_s_at 5-OXOPROLINASE(ATP- NM_017570 NP_060040 HYDROLYSING) ORF1-FL49 224707_at PUTATIVENUCLEAR NM_032412 NP_115788 PROTEIN ORF1-FL49 OSM 230170_at ONCOSTATIN MNM_020530 NP_065391 OSTalpha 229230_at ORGANIC SOLUTE NM_152672NP_689885 TRANSPORTER ALPHA OTUD1 226140_s_at OTU DOMAIN XM_166659XP_166659 CONTAINING 1 P2RX1 210401_at PURINERGIC RECEPTOR NM_002558NP_002549 P2X, LIGAND-GATED ION CHANNEL, 1 PAG 225622_at PHOSPHOPROTEINNM_018440 NP_060910 ASSOCIATED WITH GLYCOSPHINGOLIPID- ENRICHEDMICRODOMAINS PAM 202336_s_at PEPTIDYLGLYCINE NM_000919 NP_000910ALPHA-AMIDATING NM_138766 NP_620121 MONOOXYGENASE NM_138821 NP_620176NM_138822 NP_620177 PAPSS1 209043_at 3′-PHOSPHOADENOSINE NM_005443NP_005434 5′-PHOSPHOSULFATE SYNTHASE 1 PBEF1 217738_at PRE-B-CELL COLONYNM_005746 NP_005737 ENHANCING FACTOR 1 NM_182790 NP_877591 PBEF1217739_s_at PRE-B-CELL COLONY NM_005746 NP_005737 ENHANCING FACTOR 1NM_182790 NP_877591 PBEF1 1555167_s_at PRE-B-CELL COLONY NM_005746NP_005737 ENHANCING FACTOR 1 NM_182790 NP_877591 PBEF1 243296_atPRE-B-CELL COLONY NM_005746 NP_005737 ENHANCING FACTOR 1 NM_182790NP_877591 PCGF5 227935_s_at POLYCOMB GROUP RING NM_032373 NP_115749FINGER 5 PCMT1 208857_s_at PROTEIN-L- NM_005389 NP_005380 ISOASPARTATE(D- ASPARTATE) O- METHYLTRANSFERASE PCMT1 210156_s_at PROTEIN-L-NM_005389 NP_005380 ISOASPARTATE (D- ASPARTATE) O- METHYLTRANSFERASEPCMT1 205202_at PROTEIN-L- NM_005389 NP_005380 ISOASPARTATE (D-ASPARTATE) O- METHYLTRANSFERASE PDCD1LG1 227458_at CD274 ANTIGEN (CD274)NM_014143 NP_054862 PDCD1LG1 223834_at CD274 ANTIGEN (CD274) NM_014143NP_054862 PDE5A 239556_at PHOSPHODIESTERASE 5A, NM_001083 NP_001074CGMP-SPECIFIC NM_033430 NP_236914 NM_033437 NP_246273 NM_033431NP_237223 PDK1 226452_at PYRUVATE NM_002610 NP_002601 DEHYDROGENASEKINASE, ISOENZYME 1 PEA15 200788_s_at PHOSPHOPROTEIN NM_003768 NP_003759ENRICHED IN ASTROCYTES 15 PFKFB2 209992_at 6-PHOSPHOFRUCTO-2- NM_006212NP_006203 KINASE/FRUCTOSE-2,6- BIPHOSPHATASE 2 PFKFB2 226733_at6-PHOSPHOFRUCTO-2- NM_006212 NP_006203 KINASE/FRUCTOSE-2,6-BIPHOSPHATASE 2 PFKFB3 202464_s_at 6-PHOSPHOFRUCTO-2- NM_004566NP_004557 KINASE/FRUCTOSE-2,6- BISPHOSPHATASE 3 PFTK1 204604_at PFTAIREPROTEIN NM_012395 NP_036527 KINASE 1 PGLYRP1 207384_at PEPTIDOGLYCANNM_005091 NP_005082 RECOGNITION PROTEIN 1 PGM2 225366_atPHOSPHOGLUCOMUTASE 2 NM_018290 NP_060760 PGS1 219394_atPHOSPHATIDYLGLYCERO- NM_024419 NP_077733 PHOSPHATE SYNTHASE PHTF1210191_s_at PUTATIVE NM_006608 NP_006599 HOMEODOMAIN TRANSCRIPTIONFACTOR 1 PHTF1 215285_s_at PUTATIVE NM_006608 NP_006599 HOMEODOMAINTRANSCRIPTION FACTOR 1 PHTF1 205702_at PUTATIVE NM_006608 NP_006599HOMEODOMAIN TRANSCRIPTION FACTOR 1 PIK3AP1 226459_at PHOSPHOINOSITIDE-3-NM_152309 NP_689522 KINASE ADAPTOR PROTEIN 1 PIK3CB 212688_atPHOSPHOINOSITIDE-3- NM_006219 NP_006210 KINASE, CATALYTIC, BETAPOLYPEPTIDE PIM3 224739_at PIM-3 ONCOGENE NM_001001852 NP_001001852PIP5K1A 235646_at PHOSPHATIDYLINOSITOL- NM_003557 NP_003548 4-PHOSPHATE5-KINASE, TYPE I, ALPHA PLSCR1 244315_at PHOSPHOLIPID NM_021105NP_066928 SCRAMBLASE 1 PLSCR1 241916_at PHOSPHOLIPID NM_021105 NP_066928SCRAMBLASE 1 POR 208928_at P450 (CYTOCHROME) NM_000941 NP_000932OXIDOREDUCTASE PPP1R12A 201602_s_at PROTEIN PHOSPHATASE NM_002480NP_002471 1, REGULATORY (INHIBITOR) SUBUNIT 12A PPP4R2 225519_at PROTEINPHOSPHATASE NM_174907 NP_777567 4, REGULATORY SUBUNIT 2 PPP4R2 226317_atPROTEIN PHOSPHATASE NM_174907 NP_777567 4, REGULATORY SUBUNIT 2 PRO0149225183_at PRO0149 PROTEIN NM_014117 NP_054836 PRV1 219669_atNEUTROPHIL-SPECIFIC NM_020406 NP_065139 ANTIGEN 1 (POLYCYTHEMIA RUBRAVERA 1) PSTPIP2 219938_s_at PROLINE/SERINE/THREONINE NM_024430 NP_077748PHOSPHATASE- INTERACTING PROTEIN 1 (PROLINE-SERINE- THREONINEPHOSPHATASE INTERACTING PROTEIN 2) PTDSR 212723_at CHROMOSOME 17NM_015167 NP_055982 GENOMIC CONTIG, ALTERNATE ASSEMBLY(PHOSPHATIDYLSERINE RECEPTOR) PTDSR 212722_s_at CHROMOSOME 17 NM_015167NP_055982 GENOMIC CONTIG, ALTERNATE ASSEMBLY (PHOSPHATIDYLSERINERECEPTOR) PTGFR 207177_at CHROMOSOME 17 NM_015167 NP_055982 GENOMICCONTIG, ALTERNATE ASSEMBLY (PHOSPHATIDYLSERINE RECEPTOR) PTPN1 202716_atPROTEIN TYROSINE NM_002827 NP_002818 PHOSPHATASE, NON- RECEPTOR TYPE 1PTX1 226422_at PTX1 PROTEIN NM_016570 NP_057654 QSCN6 201482_at QUIESCINQ6 NM_001004128 NP_001004128 NM_002826 NP_002817 RAB10 222981_s_atRAB10, MEMBER RAS NM_016131 NP_057215 ONCOGENE FAMILY RAB20 219622_atRAB20, MEMBER RAS NM_017817 NP_060287 ONCOGENE FAMILY RAB24 225251_atRAB24, MEMBER RAS NM_130781 NP_570137 ONCOGENE FAMILY RAB27A 209514_s_atRAB27A, MEMBER RAS NM_004580 NP_004571 ONCOGENE FAMILY NM_183234NP_899057 NM_183235 NP_899058 NM_183236 NP_899059 RAB27A 210951_x_atRAB27A, MEMBER RAS NM_004580 NP_004571 ONCOGENE FAMILY NM_183234NP_899057 NM_183235 NP_899058 NM_183236 NP_899059 RAB43 225632_s_atRAB43, MEMBER RAS NM_198490 NP_940892 ONCOGENE FAMILY RAB8B 219210_s_atRAB8B, MEMBER RAS NM_016530 NP_057614 ONCOGENE FAMILY RABGEF1 218310_atRAB GUANINE NM_014504 NP_055319 NUCLEOTIDE EXCHANGE FACTOR (RAB GUANINENUCLEOTIDE EXCHANGE FACTOR (GEF) 1) RAD23B 201222_s_at RAD23 HOMOLOG B(S. CEREVISIAE) NM_002874 NP_002865 RALB 202101_s_at V-RAL SIMIANLEUKEMIA NM_002881 NP_002872 VIRAL ONCOGENE HOMOLOG B (RAS RELATED; GTPBINDING PROTEIN) RAPGEFL1 218657_at RAP GUANINE NM_014504 NP_055319NUCLEOTIDE EXCHANGE FACTOR (GEF)-LIKE 1 RARA 228037_at RETINOIC ACIDNM_000964 NP_000955 RECEPTOR, ALPHA RASSF4 226436_at RAS ASSOCIATIONNM_032023 NP_114412 (RALGDS/AF-6) DOMAIN NM_178145 NP_835281 FAMILY 4RB1CC1 202033_s_at RB1-INDUCIBLE COILED- NM_014781 NP_055596 COIL 1RBMS1 225265_at RNA BINDING MOTIF, NM_002897 NP_002888 SINGLE STRANDEDNM_016836 NP_058520 INTERACTING PROTEIN 1 NM_016839 NP_058523 RBMS1238317_x_at RNA BINDING MOTIF, NM_002897 NP_002888 SINGLE STRANDEDNM_016836 NP_058520 INTERACTING PROTEIN 1 NM_016839 NP_058523 RFWD2234950_s_at RING FINGER AND WD NM_001001740 NP_001001740 REPEAT DOMAIN 2NM_022457 NP_071902 Rgr 235816_s_at RAL-GDS RELATED NM_153615 NP_705843PROTEIN RGR RGS10 204319_s_at REGULATOR OF G- NM_001005339 NP_001005339PROTEIN SIGNALLING 10 NM_002925 NP_002916 RHOT1 222148_s_at RAS HOMOLOGGENE NM_018307 NP_060777 FAMILY, MEMBER T1 RIT1 209882_at RAS-LIKEWITHOUT NM_006912 NP_008843 CAAX 1 RNASE6 213566_at RIBONUCLEASE, RNASEA NM_005615 NP_005606 FAMILY, K6 RNASEL 229285_at RIBONUCLEASE L (2′,5′-NM_021133 NP_066956 OLIGOISOADENYLATE SYNTHETASE- DEPENDENT) RNF13201779_s_at RING FINGER PROTEIN 13 NM_007282 NP_009213 NM_183381NP_899237 NM_183382 NP_899238 NM_183383 NP_899239 NM_183384 NP_899240RNF13 201780_s_at RING FINGER PROTEIN 13 NM_007282 NP_009213 NM_183381NP_899237 NM_183382 NP_899238 NM_183383 NP_899239 NM_183384 NP_899240ROD1 224618_at ROD1 REGULATOR OF NM_005156 NP_005147 DIFFERENTIATION 1(S. POMBE) ROD1 214697_s_at ROD1 REGULATOR OF NM_005156 NP_005147DIFFERENTIATION 1 (S. POMBE) RRM2 209773_s_at RIBONUCLEOTIDE NM_001034NP_001025 REDUCTASE M2 POLYPEPTIDE RSBN1 213694_at ROUND SPERMATIDNM_018364 NP_060834 BASIC PROTEIN 1 RTN1 203485_at RETICULON 1 NM_021136NP_066959 NM_206852 NP_996734 NM_206857 NP_996739 RTN4 214629_x_atRETICULON 4 NM_007008 NP_008939 NM_020532 NP_065393 NM_153828 NP_722550NM_207520 NP_997403 NM_207521 NP_997404 RY1 212438_at PUTATIVE NUCLEICACID NM_006857 NP_006848 BINDING PROTEIN RY-1 S100A12 205863_at S100CALCIUM BINDING NM_005621 NP_005612 PROTEIN A12 (CALGRANULIN C) SAMSN11555638_a_at SAM DOMAIN, SH3 NM_022136 NP_071419 DOMAIN, AND NUCLEARLOCALIZATION SIGNALS, 1 SAMSN1 220330_s_at SAM DOMAIN, SH3 NM_022136NP_071419 DOMAIN, AND NUCLEAR LOCALIZATION SIGNALS, 1 SAP30L 219129_s_atSIN3A ASSOCIATED NM_024632 NP_078908 PROTEIN P30-LIKE SART2 218854_atSQUAMOUS CELL NM_013352 NP_037484 CARCINOMA ANTIGEN RECOGNIZED BY TCELLS 2 SBNO1 218737_at SNO, STRAWBERRY NM_018183 NP_060653 NOTCHHOMOLOG 1 (DROSOPHILA) SDF2 203090_at STROMAL CELL-DERIVED NM_006923NP_008854 FACTOR 2 SDHC 238056_at SUCCINATE NM_003001 NP_002992DEHYDROGENASE COMPLEX, SUBUNIT C, INTEGRAL MEMBRANE PROTEIN, 15 KDASEC15L1 226259_at SEC15-LIKE 1 (S. CEREVISIAE) NM_019053 NP_061926NM_001013848 NP_001013870 SEC15L1 233924_s_at SEC15-LIKE 1 (S.CEREVISIAE) NM_019053 NP_061926 NM_001013848 NP_001013870 SEC24A212902_at SEC24 RELATED GENE XM_094581 XP_094581 FAMILY, MEMBER A (S.CEREVISIAE) SEL1L 202064_s_at SEL-1 SUPPRESSOR OF NM_005065 NP_005056LIN-12-LIKE (C. ELEGANS) SERPINB1 228726_at SERINE (OR CYSTEINE)NM_030666 NP_109591 PROTEINASE INHIBITOR, CLADE B (OVALBUMIN), MEMBER 1SERPINB1 212268_at SERINE (OR CYSTEINE) NM_030666 NP_109591 PROTEINASEINHIBITOR, CLADE B (OVALBUMIN), MEMBER 1 SF3B14 223416_at SPLICINGFACTOR 3B, 14 KDA NM_016047 NP_057131 SUBUNIT SH3GLB1 209091_s_atSH3-DOMAIN GRB2-LIKE NM_016009 NP_057093 ENDOPHILIN B1 SH3GLB1210101_x_at SH3-DOMAIN GRB2-LIKE NM_016009 NP_057093 ENDOPHILIN B1SIPA1L2 225056_at SIGNAL-INDUCED NM_020808 NP_065859 PROLIFERATION-ASSOCIATED GENE 1 (SIGNAL-INDUCED PROLIFERATION- ASSOCIATED 1 LIKE 2)SLA 203761_at SRC-LIKE-ADAPTOR NM_006748 NP_006739 SLA 244492_atSRC-LIKE-ADAPTOR NM_006748 NP_006739 SLC22A4 205896_at SOLUTE CARRIERNM_003059 NP_003050 FAMILY 22 (ORGANIC CATION TRANSPORTER), MEMBER 4SLC25A28 221432_s_at SOLUTE CARRIER NM_031212 NP_112489 FAMILY 25,MEMBER 28 SLC26A8 237340_at SOLUTE CARRIER NM_052961 NP_443193 FAMILY26, MEMBER 8 NM_138718 NP_619732 SLC2A3 202497_x_at SOLUTE CARRIERNM_006931 NP_008862 FAMILY 2 (FACILITATED GLUCOSE TRANSPORTER), MEMBER 3SLC2A3 202498_s_at SOLUTE CARRIER NM_006931 NP_008862 FAMILY 2(FACILITATED GLUCOSE TRANSPORTER), MEMBER 3 SLC2A3 216236_s_at SOLUTECARRIER NM_006931 NP_008862 FAMILY 2 (FACILITATED GLUCOSE TRANSPORTER),MEMBER 3 SLC2A3 202499_s_at SOLUTE CARRIER NM_006931 NP_008862 FAMILY 2(FACILITATED GLUCOSE TRANSPORTER), MEMBER 3 SLC2A3 222088_s_at SOLUTECARRIER NM_006931 NP_008862 FAMILY 2 (FACILITATED GLUCOSE TRANSPORTER),MEMBER 3 SLC2A3 236571_at SOLUTE CARRIER NM_006931 NP_008862 FAMILY 2(FACILITATED GLUCOSE TRANSPORTER), MEMBER 3 SLC37A3 223304_at SOLUTECARRIER NM_032295 NP_115671 FAMILY 37 (GLYCEROL-3- NM_207113 NP_996996PHOSPHATE TRANSPORTER), MEMBER 3 SLC38A2 220924_s_at SOLUTE CARRIERNM_018976 NP_061849 FAMILY 38, MEMBER 2 SMPDL3A 213624_at SPHINGOMYELINNM_006714 NP_006705 PHOSPHODIESTERASE, ACID-LIKE 3A SOCS3 227697_atSUPPRESSOR OF NM_003955 NP_003946 CYTOKINE SIGNALING 3 SOCS3 206359_atSUPPRESSOR OF NM_003955 NP_003946 CYTOKINE SIGNALING 3 SOD2 216841_s_atSUPEROXIDE DISMUTASE NM_000636 NP_000627 2, MITOCHONDRIAL SP100202863_at NUCLEAR ANTIGEN SP100 NM_003113 NP_003104 SPPL2A 226353_atSIGNAL PEPTIDE NM_032802 NP_116191 PEPTIDASE-LIKE 2A SQRDL 217995_atSULFIDE QUINONE NM_021199 NP_067022 REDUCTASE-LIKE (YEAST) SRPK1202200_s_at PROTEIN KINASE, NM_003137 NP_003128 SERINE/ARGININE-SPECIFIC, 1 (SFRS PROTEIN KINASE 1) ST3GAL4 203759_at ST3BETA-GALACTOSIDE NM_006278 NP_006269 ALPHA-2,3- SIALYLTRANSFERASE 4ST6GALNAC4 223285_s_at ST6 (ALPHA-N-ACETYL- NM_014403 NP_055218NEURAMINYL-2,3-BETA- NM_175039 NP_778204 GALACTOSYL-1,3)-N- NM_175040NP_778205 ACETYLGALACTOSAMINIDE ALPHA-2,6- SIALYLTRANSFERASE 4 STAT5B1555086_at SIGNAL TRANSDUCER NM_012448 NP_036580 AND ACTIVATOR OFTRANSCRIPTION 5B STK17B 205214_at SERINE/THREONINE NM_004226 NP_004217KINASE 17B (APOPTOSIS- INDUCING) STK3 204068_at SERINE/THREONINENM_006281 NP_006272 PROTEIN KINASE 3 (SERINE/THREONINE KINASE 3 (STE20HOMOLOG, YEAST)) STOM 201060_x_at STOMATIN NM_004099 NP_004090 NM_198194NP_937837 STX3A 209238_at SYNTAXIN 3A NM_004177 NP_004168 SULF2233555_s_at SULFATASE 2 NM_018837 NP_061325 NM_198596 NP_940998 SULF2224724_at SULFATASE 2 NM_018837 NP_061325 NM_198596 NP_940998 SULT1A1203615_x_at SULFOTRANSFERASE NM_001055 NP_001046 FAMILY, CYTOSOLIC, 1A,NM_177529 NP_803565 PHENOL-PREFERRING, NM_177530 NP_803566 MEMBER 1NM_177534 NP_803878 NM_177536 NP_803880 SULT1B1 207601_atSULFOTRANSFERASE NM_014465 NP_055280 FAMILY, CYTOSOLIC, 1B, MEMBER 1TBC1D15 218268_at TBC1 DOMAIN FAMILY, NM_022771 NP_073608 MEMBER 15TBC1D8 204526_s_at TBC1 DOMAIN FAMILY, NM_007063 NP_008994 MEMBER 8(WITH GRAM DOMAIN) TCTEL1 201999_s_at T-COMPLEX- NM_006519 NP_006510ASSOCIATED-TESTIS- EXPRESSED 1 (T- COMPLEX-ASSOCIATED- TESTIS-EXPRESSED1- LIKE 1 TCTEL1 242109_at T-COMPLEX- NM_006519 NP_006510ASSOCIATED-TESTIS- EXPRESSED 1 (T- COMPLEX-ASSOCIATED- TESTIS-EXPRESSED1- LIKE 1 TDRD9 228285_at TUDOR DOMAIN NM_153046 NP_694591 CONTAINING 9TGFBI 201506_at TRANSFORMING NM_000358 NP_000349 GROWTH FACTOR, BETA- 1(TRANSFORMING GROWTH FACTOR, BETA- INDUCED, 68 KDA) TIAM2 222942_s_atT-CELL LYMPHOMA NM_001010927 NP_001010927 INVASION AND NM_012454NP_036586 METASTASIS 2 TIFA 238858_at TRAF-INTERACTING NM_052864NP_443096 PROTEIN WITH A FORKHEAD-ASSOCIATED DOMAIN TIFA 226117_atTRAF-INTERACTING NM_052864 NP_443096 PROTEIN WITH A FORKHEAD-ASSOCIATEDDOMAIN TIFA 235971_at TRAF-INTERACTING NM_052864 NP_443096 PROTEIN WITHA FORKHEAD-ASSOCIATED DOMAIN TLR5 210166_at TOLL-LIKE RECEPTOR 5NM_003268 NP_003259 TMEM2 218113_at TRANSMEMBRANE NM_013390 NP_037522PROTEIN 2 TMEM33 235907_at TRANSMEMBRANE NM_018126 NP_060596 PROTEIN 33TMOD3 223078_s_at TROPOMODULIN 3 NM_014547 NP_055362 (UBIQUITOUS) TncRNA214657_s_at TROPHOBLAST-DERIVED NONCODING RNA TNFAIP6 206026_s_at TUMORNECROSIS NM_007115 NP_009046 FACTOR, ALPHA- INDUCED PROTEIN 6 TNFAIP6206025_s_at TUMOR NECROSIS NM_007115 NP_009046 FACTOR, ALPHA- INDUCEDPROTEIN 6 TNFAIP9 225987_at TUMOR NECROSIS NM_024636 NP_078912 FACTOR,ALPHA- INDUCED PROTEIN 9 TNFSF10 202687_s_at TUMOR NECROSIS NM_003810NP_003801 FACTOR (LIGAND) SUPERFAMILY, MEMBER 10 TNFSF13B 223501_atTUMOR NECROSIS NM_006573 NP_006564 FACTOR (LIGAND) SUPERFAMILY, MEMBER13B TOP1 208901_s_at TOPOISOMERASE (DNA) I NM_003286 NP_003277 TOSO221602_s_at FAS APOPTOTIC NM_005449 NP_005440 INHIBITORY MOLECULE(FAIM3) TPARL 218095_s_at TPA REGULATED LOCUS NM_018475 NP_060945 TPCN1242108_at TWO PORE SEGMENT NM_017901 NP_060371 CHANNEL 1 TPRT220865_s_at TRANS- NM_014317 NP_055132 PRENYLTRANSFERASE TRA@ 234013_atT CELL RECEPTOR ALPHA LOCUS TRA@ 211902_x_at T CELL RECEPTOR ALPHA LOCUSTRBC1 211796_s_at T CELL RECEPTOR BETA CONSTANT 1 TRIB1 202241_atTRIBBLES HOMOLOG 1 NM_025195 NP_079471 (DROSOPHILA) TRPM6 224412_s_atTRANSIENT RECEPTOR NM_017662 NP_060132 POTENTIAL CATION CHANNEL,SUBFAMILY M, MEMBER 6 TTN 240793_at TITIN NM_003319 NP_003310 NM_133378NP_596869 NM_133379 NP_596870 NM_133432 NP_597676 NM_133437 NP_597681TTYH2 223741_s_at TWEETY, DROSOPHILA, NM_032646 NP_116035 HOMOLOG OF, 2NM_052869 NP_443101 TXN 208864_s_at THIOREDOXIN NM_003329 NP_003320UBE2H 222421_at UBIQUITIN- NM_003344 NP_003335 CONJUGATING ENZYMENM_182697 NP_874356 E2H (UBC8 HOMOLOG, YEAST) UBE2J1 217826_s_atUBIQUITIN- NM_016021 NP_057105 CONJUGATING ENZYME E2, J1 (UBC6 HOMOLOG,YEAST) UBQLN2 215884_s_at UBIQUILIN 2 NM_013444 NP_038472 UNC84B229548_at UNC-84 HOMOLOG B (C. ELEGANS) NM_015374 NP_056189 USP38223289_s_at UBIQUITIN SPECIFIC NM_032557 NP_115946 PROTEASE 38 USP9X201099_at UBIQUITIN SPECIFIC NM_004652 NP_004643 PROTEASE 9, X-LINKEDNM_021906 NP_068706 (FAT FACETS-LIKE, DROSOPHILA) VAV3 218807_at VAV 3ONCOGENE NM_006113 NP_006104 WBP4 203598_s_at WW DOMAIN BINDINGNM_007187 NP_009118 PROTEIN 4 (FORMIN BINDING PROTEIN 21) WDFY3212606_at WD REPEAT AND FYVE NM_014991 NP_055806 DOMAIN CONTAINING 3NM_178583 NP_848698 NM_178585 NP_848700 WDFY3 212602_at WD REPEAT ANDFYVE NM_014991 NP_055806 DOMAIN CONTAINING 3 NM_178583 NP_848698NM_178585 NP_848700 WDFY3 212598_at WD REPEAT AND FYVE NM_014991NP_055806 DOMAIN CONTAINING 3 NM_178583 NP_848698 NM_178585 NP_848700WSB1 227501_at WD REPEAT AND SOCS NM_015626 NP_056441 BOX-CONTAINING 1NM_134264 NP_599026 NM_134265 NP_599027 WSB1 210561_s_at WD REPEAT ANDSOCS NM_015626 NP_056441 BOX-CONTAINING 1 NM_134264 NP_599026 NM_134265NP_599027 WSB1 201296_s_at WD REPEAT AND SOCS NM_015626 NP_056441BOX-CONTAINING 1 NM_134264 NP_599026 NM_134265 NP_599027 XRN11555785_a_at 5′-3′ EXORIBONUCLEASE 1 NM_019001 NP_061874 XRN1233632_s_at 5′-3′ EXORIBONUCLEASE 1 NM_019001 NP_061874 ZC3HAV1225634_at ZINC FINGER CCCH TYPE, NM_020119 NP_064504 ANTIVIRAL 1NM_024625 NP_078901 ZCSL2 225195_at ZINC FINGER, CSL NM_206831 NP_996662DOMAIN CONTAINING 2 ZDHHC19 231122_x_at ZINC FINGER, DHHC NM_144637NP_653238 DOMAIN CONTAINING 19 ZDHHC19 1553952_at ZINC FINGER, DHHCNM_144637 NP_653238 DOMAIN CONTAINING 19 ZFP276 213778_x_at ZINC FINGERPROTEIN 276 NM_152287 NP_689500 HOMOLOG (MOUSE) ZFP36L2 201367_s_at ZINCFINGER PROTEIN 36, NM_006887 NP_008818 C3H TYPE-LIKE 2; ZFP36L2 ZFP36L2201369_s_at ZINC FINGER PROTEIN 36, NM_006887 NP_008818 C3H TYPE-LIKE 2;ZFP36L2 ZNF167 206314_at ZINC FINGER PROTEIN 167 NM_018651 NP_061121NM_025169 NP_079445 230585_at EST 200880_at EST 230267_at EST215966_x_at EST 237071_at EST 230683_at EST 241388_at EST 204166_at EST241652_x_at EST 229968_at EST 223596_at EST 240310_at EST 216609_at EST224604_at EST 223797_at EST 238973_s_at EST 230632_at EST 230575_at EST1559777_at EST 244313_at EST 242582_at EST 233264_at EST 219253_at EST235427_at EST 1555311_at EST 229934_at EST 231035_s_at EST 230999_at EST224261_at EST 239780_at EST 239669_at EST 213002_at EST 227925_at EST235456_at EST 233312_at EST 239167_at EST 1569263_at EST 216198_at EST232876_at EST 237387_at EST 216621_at EST 235352_at EST 1564933_at EST222376_at EST 205922_at EST 1557626_at EST 228758_at EST 1557733_a_atEST 236898_at EST

Each of the sequences, genes, proteins, and probesets identified inTable 30 is hereby incorporated by reference.

6.7 Exemplary Biomarker Combinations

In one embodiment of the present invention, an additional criterion wasapplied to the set of biomarkers identified in Section 6.6.Specifically, the additional criterion that was imposed was arequirement that each respective biomarker under consideration exhibitat least a 1.2× fold change between the median value for the respectivebiomarker among the subjects that acquired sepsis during a defined timeperiod (sepsis subjects) and the median value for the respectivebiomarker among subjects that do not acquire sepsis during the definedtime period (SIRS subjects) at the T⁻¹² static time and at the T⁻³⁶static time periods. Furthermore, to satisfy the third criterion, thebiomarker must have been used in at least one multivariate analysis withsignificant classification performance where significant classificationperformance is defined by having a lower 95^(th) percentile for accuracyon a training data set that is grater than 50% and a point estimate foraccuracy on the validation set that is greater than 65% at any timepoint measured. As noted in FIG. 30, application of this third filtercriterion reduced the number of eligible biomarkers from 626 to 130.These biomarkers are listed column two of Table 31. In column two ofTable 31, the biomarkers are indicated by the U133 plus 2.0 probeset towhich they bind. However, in some embodiments, each such biomarker is,in fact, an mRNA, cDNA, or other such nucleic acid moleculecorresponding to the identified U133 plus 2.0 oligonucleotide probelisted in column two of Table 31.

In column one of Table 31, each biomarker is listed by a gene name, suchas, for example, a Human Gene Nomenclature Database (HUGO) symbol setforth by the Gene Nomenclature Committee, Department of Biology,University College London. As is known in the art, some human genomegenes are represented by more than one probeset in the U133 plus 2.0array. Furthermore, some of the oligonucleotides in the U133 plus 2.0array represent expression sequence tags (ESTs) that do not correspondto a known gene. As a result, the 130 biomarkers listed in Table 31, infact, represent 95 different known genes (see FIG. 30). Where known, thenames of the 95 different human genes are listed in column three ofTable 31.

In column four of Table 31, the median fold change between the meanvalue of the biomarker measured from T⁻¹² samples of those subjects inthe training population that develop sepsis (sepsis subjects) versus themean value of the biomarker measured from T⁻¹² samples of those subjectsin the training population that do not develop sepsis (SIRS subjects) isgiven. In column five of Table 31, the direction of the fold change,where “+” indicates that the mean value in the sepsis subjects isgreater than in the SIRS subjects, is given.

In column six of Table 31, the median fold change between the mean valueof the biomarker measured from T⁻³⁶ samples of those subjects in thetraining population that develop sepsis (sepsis subjects) versus themean value of the biomarker measured from T⁻³⁶ samples of those subjectsin the training population that do not develop sepsis (SIRS subjects) isgiven. In column seven of Table 31, the direction of the fold change,where “+” indicates that the mean value in the sepsis subjects isgreater than in the SIRS subjects, is given.

In a particular embodiment, the biomarker profile comprises at least twodifferent biomarkers that each contain one of the probesets of Table 32,biomarkers that contain the complement of one of the probesets of Table32, or biomarkers that contain an amino acid sequence encoded by a genethat contains one of the probesets of Table 32. Such biomarkers can be,for example, mRNA transcripts, cDNA or some other nucleic acid, forexample amplified nucleic acid, or proteins. The biomarker profilefurther comprises a respective corresponding feature for the at leasttwo biomarkers. Generally, the at least two biomarkers are derived fromat least two different genes. In the case where a biomarker is basedupon a gene that includes the sequence of a probeset listed in Table 32or a complement thereof, the biomarker can be, for example, a transcriptmade by the gene, a complement thereof, or a discriminating fragment orcomplement thereof, or a cDNA thereof, or a discriminating fragment ofthe cDNA, or a discriminating amplified nucleic acid moleculecorresponding to all or a portion of the transcript or its complement,or a protein encoded by the gene, or a discriminating fragment of theprotein, or an indication of any of the above. Further still, thebiomarker can be, for example, a protein encoded by a gene that includesa probeset sequence described in Table 32 or a discriminating fragmentof the protein, or an indication of the above. Here, a discriminatingmolecule or fragment is a molecule or fragment that, when detected,indicates presence or abundance of the above-identified transcript,cDNA, amplified nucleic acid, or protein. In one embodiment, a biomarkerprofile of the present invention comprises a plurality of biomarkersthat contain at least five, at least ten at least fifteen, at leasttwenty, at least thirty, between 2 and 5, between 3 and 7, or less than15 of the sequences of the probesets of Table 32, or complementsthereof, or genes including one of at least five of the sequences orcomplements thereof, or a discriminating fragment thereof, or an aminoacid sequence encoded by any of the foregoing nucleic acid sequences, orany discriminating fragment of such an amino acid sequence. Suchbiomarkers can be, for example, mRNA transcripts, cDNA or some othernucleic acid, for example amplified nucleic acid, or proteins. In someembodiments a biomarker is any gene that includes the sequence in anAffymetrix probeset given in Table 31, or any gene that includes acomplement of the sequence in an Affymetrix probeset given in Table 32,or any mRNA, cDNA or other form of amplified nucleic acid of theforegoing, for any discriminating fragment of the foregoing, or anyamino acid sequence coded by the foregoing, or any discriminatingfragment of such a protein.

TABLE 31 Exemplary biomarkers that discriminate between converters andnon-converters T⁻¹² Values T⁻³⁶ Values Gene Affymetrix Gene MedianMedian Symbol Probeset name Name FC Direction FC Direction Column ColumnColumn Column Column Column Column 1 2 3 3 4 5 6 1555785_a_at EST 1.34Up 1.31 up  227150_at EST 1.45 Up 1.34 up  238973_s_at EST 1.33 Up 1.25up  239893_at EST 1.89 Up 1.46 up  237563_s_at EST 1.84 Up 1.60 up 244313_at EST 1.87 Up 1.80 up  237071_at EST 1.73 up 1.37 up  229934_atEST 1.67 up 1.41 up 1555311_at EST 1.31 up 1.21 up  233264_at EST 1.52up 1.36 up  239780_at EST 1.93 up 1.42 up  238405_at EST 3.02 up 2.23 up3′HEXO  226416_at HISTONE 1.70 up 1.41 up MRNA 3′ END EXORIBO- NUCLEASE3′HEXO  231852_at 1.45 up 1.22 up ADORA2A  205013_s_at ADENOSINE 1.32 up1.33 up A2 RECEPTOR ANXA3  209369_at ANNEXIN A3 2.82 up 2.23 up ASAHL 214765_s_at N- 1.28 down 1.29 down ACYLSPHIN- GOSINE AMIDO- HYDROLASE-LIKE PROTEIN ASAHL  232072_at 1.23 down 1.31 down ASAHL  227135_at 1.24down 1.26 down ATP11B 1554557_at ATPASE, 1.70 up 1.49 up CLASS VI, TYPE11B ATP6V1C1  202872_at ATPASE, H+ 1.85 up 1.49 up TRANSPORT- ING,LYSOSOMAL, 42-KD, V1 SUBUNIT C, ISOFORM 1 B4GALT5  221485_at BETA-1,4-1.67 up 1.43 up GALACTOSYL- TRANSFER- ASE BASP1  202391_at BRAIN- 1.42up 1.23 up ABUNDANT SIGNAL PROTEIN BAZ1A  217986_s_at BROMODOMA 1.89 up1.57 up IN ADJACENT TO ZINC FINGER DOMAIN, 1A BCL6  203140_at B-CELL1.46 up 1.33 up LYMPHOMA 6 BMX  206464_at BONE 1.87 up 1.52 up MARROWKINASE, X- LINKED C16orf7  205781_at CHROMOSOME 1.96 up 1.49 up 16 OPEN-READING FRAME 7 C20orf32 1554786_at CHROMOSOME 1.27 down 1.24 down 20OPEN- READING FRAME 32 C3F  203547_at COMPLEMENT 1.28 down 1.26 downCOMPONENT 3 C8FW  202241_at C8FW GENE; 1.57 up 1.34 up PHOSPHO- PROTEIN.CEACAM1  209498_at CARCINO- 2.78 up 2.20 up EMBRYONIC ANTIGEN- RELATEDCELL ADHESION MOLECULE 1 CEACAM1  211883_x_at 1.87 up 1.61 up CECR1 219505_at CAT EYE 1.25 down 1.26 down SYNDROME CHROMOSOME REGION,CANDIDATE 1 CHCHD7  222701_s_at COILED-COIL- 1.58 up 1.26 up HELIXDOMAIN- CONTAINING PROTEIN 7 CHSY1  203044_at CARBO- 1.78 up 1.24 upHYDRATE SYNTHASE 1 CKLF  223451_s_at CHEMOKINE- 1.59 up 1.40 up LIKEFACTOR CKLF  219161_s_at 1.38 up 1.31 up CL25022  217883_at 1.36 up 1.31up CPD  201940_at CARBOXY- 1.61 up 1.38 up PEPTIDASE D CPD  201941_at1.61 up 1.35 up CRTAP 1554464_a_at CARTILAGE- 1.23 down 1.21 downASSOCIATED PROTEIN DHRS9  219799_s_at MEMBRANE 1.94 up 1.52 up PROTEIN,PALM- ITOYLATED 3; MPP3 EIF4G3  201936_s_at EUKARYOTIC 1.58 up 1.28 upTRANSLATION INITIATION FACTOR 4- GAMMA, 3 FAD104  218618_s_atFIBRONECTIN 1.61 up 1.38 up TYPE III DOMAIN CONTAINING 3B (FNDC3B FAD104 225032_at 1.63 up 1.41 up FAD104  222692_s_at 1.74 up 1.59 up FAD104 222693_at 1.88 up 1.92 up FCGR1A  214511_x_at FC 2.37 up 1.71 upFRAGMENT OF IGG, HIGH AFFINITY IA FCGR1A  216950_s_at 2.56 up 1.60 upFLJ11175  229005_at 1.77 up 1.50 up FLJ11175  220603_s_at 2.12 up 1.77up FLJ11259  218627_at 1.54 up 1.25 up FLJ11795  220112_at 1.80 up 1.62up FLJ22833  219334_s_at 1.49 up 1.28 up G0S2  213524_s_at 1.69 up 1.30up GADD45B  207574_s_at GROWTH 1.55 up 1.37 up ARREST- AND DNA DAMAGE-INDUCIBLE GENE GADD45 GADD45B  209304_x_at 1.42 up 1.25 up GK  214681_atGLYCEROL 1.76 up 1.39 up KINASE GPR160  223423_at G PROTEIN- 1.83 up1.54 up COUPLED RECEPTOR 160 HLA-DMA  217478_s_at HLA-D 1.32 down 1.27down HISTOCOMPAT- IBILITY TYPE HLA-DMB  203932_at 1.29 down 1.27 downHLA-  211991_s_at 1.34 down 1.27 down DPA1 HLA-  209823_x_at 1.31 down1.20 down DQB1 HLA-DRA  210982_s_at 1.25 down 1.24 down HLA-DRA 208894_at 1.29 down 1.23 down HLA-  215193_x_at 1.30 down 1.30 downDRB1 HLA-  209312_x_at 1.27 down 1.30 down DRB1 HLA-  204670_x_at 1.25down 1.27 down DRB4 HLA-  208306_x_at 1.27 down 1.27 down DRB4 HPGD 203913_s_at 15- 2.01 up 1.57 up HYDROXY- PROSTA- GLANDIN DEHYDRO-GENASE HRPT2  218578_at HYPERPARA- 1.43 up 1.27 up THYROIDISM 2 HSPC163 228437_at HSPC163 1.64 up 1.46 up PROTEIN HSPC163  218728_s_at 2.00 up1.52 up IDI1  204615_x_at ISOPENTENYL- 1.51 up 1.31 up DIPHOSPHATEDELTA- ISOMERASE IL18R1  206618_at INTERLEUKIN 3.18 up 2.37 up 18RECEPTOR 1 KCNE1  236407_at POTASSIUM 1.68 up 1.45 up CHANNEL, VOLTAGE-GATED, ISK- RELATED SUBFAMILY KIF1B  225878_at KINESIN 2.04 up 1.62 upFAMILY MEMBER 1B; KREMEN1  227250_at KRINGLE 2.11 up 1.32 up CONTAININGTRANS- MEMBRANE PROTEIN 1 LDLR  202068_s_at LOW DENSITY 1.55 up 1.52 upLIPOPROTEIN RECEPTOR LIMK2  202193_at LIM DOMAIN 2.05 up 1.51 up KINASE2 LOC199675  235568_at 2.91 up 2.03 up LOC284829  225669_at 1.48 up 1.30up LOC285771  237870_at 1.40 up 1.34 up LRG1  228648_at LEUCINE- 2.08 up1.62 up RICH ALPHA- 2- GLYCO- PROTEIN 1 MGC22805  239196_at NOVEL GENE2.30 up 1.86 up (MGC22805), MGC22805  238439_at 3.38 up 2.24 up MPEG1 226841_at MACROPHAGE 1.23 down 1.21 down EXPRESSED GENE 1 OAT 201599_at ORNITHINE 1.53 up 1.44 up AMINOTRANS- FERASE DEFICIENCYORF1-FL49  224707_at 1.75 up 1.65 up PDCD1LG1  227458_at PROGRAMMED 2.22up 1.86 up CELL DEATH 1 LIGAND 1 PFKFB2  226733_at 6-PHOSPHO- 1.70 up1.28 up FRUCTO- 2-KINASE PFKFB2  209992_at 1.65 up 1.43 up PFKFB3 202464_s_at 6- 3.02 up 1.95 up PHOSPHO- FRUCTO-2- KINASE/ FRUCTOSE-2,6-BISPHOSPHA- TASE 3 PGS1  219394_at PHOSPHA- 2.32 up 1.70 upTIDYLGLYCERO- PHOSPHATE SYNTHASE PHTF1  205702_at PUTATIVE 1.37 up 1.20up HOMEO- DOMAIN TRANSCRIPTION FACTOR 1 PIK3AP1  226459_at PHOSPHO- 1.64up 1.39 up INOSITIDE 3- KINASE ADAPTOR PROTEIN 1 PLSCR1  241916_atPHOSPHOLIPID 2.01 up 1.57 up SCRAMBLASE 1 PRO2852  223797_at 1.62 up1.42 up PRV1  219669_at NEUTROPHIL- 7.08 up 4.72 up SPECIFIC ANTIGEN 1PSTPIP2  219938_s_at PROLINE/ 2.54 up 1.82 up SERINE/ THREONINEPHOSPHATASE- INTERACTING PROTEIN 1 PTDSR  212723_at CHROMOSOME 1.45 up1.34 up 17 GENOMIC CONTIG, ALTERNATE ASSEMBLY RABGEF1  218310_at RABGUANINE 2.03 up 1.59 up NUCLEOTIDE EXCHANGE FACTOR RARA  228037_atRETINOIC 1.54 up 1.20 up ACID RECEPTOR, ALPHA RNASEL  229285_atRIBONUCLEASE 1.68 up 1.36 up L SAMSN1 1555638_a_at SAM DOMAIN, 2.22 up1.42 up SH3 DOMAIN, AND NUCLEAR LOCALIZATION SIGNALS 1 SAMSN1 220330_s_at 2.29 up 2.10 up SEC15L1  226259_at SEC15-LIKE 1 1.64 up1.43 up (S. CEREVISIAE) (SEC15L1), MRNA SIPA1L2  225056_at SIGNAL- 2.02up 1.52 up INDUCED PROLIFERATION- ASSOCIATED GENE 1 SLC26A8  237340_at2.01 up 1.48 up SLC2A3  202499_s_at SOLUTE 1.87 up 1.71 up CARRIERFAMILY 2 (FACILITATED GLUCOSE TRANSPORTER), MEMBER 3 SOCS3  227697_atSUPPRESSOR 2.84 up 2.15 up OF CYTOKINE SIGNALING 3 SOD2  216841_s_atSUPEROXIDE 1.56 up 1.55 up DISMUTASE 2 SPPL2A  226353_at SIGNAL 1.48 up1.51 up PEPTIDE PEPTIDASE- LIKE 2A SRPK1  202200_s_at PROTEIN 1.80 up1.46 up KINASE, SERINE/ ARGININE- SPECIFIC, 1 STK3  204068_at SERINE/1.74 up 1.33 up THREONINE PROTEIN KINASE 3 SULF2  224724_at SULFATASE 21.36 down 1.35 down (SULF2), TRANSCRIPT VARIANT 2 SULF2  233555_s_at1.37 down 1.30 down T2BP  226117_at TRAF-2 2.90 up 1.90 up BINDINGPROTEIN T2BP  235971_at 1.47 up 1.27 up T2BP  238858_at 1.32 up 1.21 upTBC1D8  204526_s_at TBC1 DOMAIN 1.55 up 1.40 up FAMILY, MEMBER 8 TCTEL1 201999_s_at T-COMPLEX- 1.46 up 1.22 up ASSOCIATED- TESTIS- EXPRESSED 1TGFBI  201506_at TRANSFORMING 1.38 down 1.41 down GROWTH FACTOR, BETA-1TTYH2  223741_s_at TWEETY, 1.28 down 1.24 down DROSOPHILA, HOMOLOG OF, 2WDFY3  212598_at WD REPEAT 1.77 up 1.35 up AND FYVE DOMAIN CONTAINING 3WDFY3  212606_at 1.56 up 1.35 up WSB1  201296_s_at WD REPEAT 1.74 up1.52 up AND SOCS BOX- CONTAINING 1 ZDHHC19  231122_x_at ZINC FINGER,2.08 up 2.13 up DHHC DOMAIN CONTAINING 19 (ZDHHC19 ZDHHC19 1553952_at1.37 up 1.38 up ZFP36L2  201367_s_at ZINC FINGER 1.23 down 1.28 downPROTEIN 36, C3H TYPE- LIKE 2; ZFP36L2

Each of the sequences, genes, proteins, and probesets identified inTable 31 is hereby incorporated by reference herein in its entirety.

Table 31, above, provides a list of select biomarkers of the presentinvention. Where known, gene names are provided. Column two of Table 32,below, provides the GenBank® database accession numbers for the humannucleotide sequences of the biomarkers listed in Table 31, where known.Column three of Table 32 further provides the GenBank® databaseaccession numbers for the corresponding amino acid sequences of thebiomarkers of Table 31, where known. The biomarkers of the presentinvention include, but are not limited to, the genes and proteinsidentified by the accession numbers of Table 32, splicing variantsthereof, discriminating fragments of mRNA, cDNA or other nucleic acidsand/or peptides corresponding to all or a discriminating portion of suchgenes and proteins, etc.

These gene and protein accession numbers are provided in order toidentify some of the biomarkers of the present invention. GenBank® isthe publicly available genetic sequence database of the NationalInstitutes of Health (NIH), and is an annotated collection of allpublicly available DNA sequences (see, e.g., Nucleic Acids Research 2004Jan. 1; 32(1):23-26, which is incorporated by reference herein in itsentirety). GenBank® is part of the International Nucleotide SequenceDatabase Collaboration, which comprises the DNA DataBank of Japan(DDBJ), the European Molecular Biology Laboratory (EMBL), and GenBank atthe National Center for Biotechnology Information (NCBI).

TABLE 32 Gene and protein accession numbers for exemplary biomarkersthat discriminate between converters and non-converters Gene ProteinAffymetrix Accession Accession Gene Symbol Probeset name Gene NameNumber Number Column 1 Column 2 Column 3 Column 4 Column 5 1555785_a_atEST <NA> 227150_at EST <NA> 238973_s_at EST <NA> 239893_at EST <NA>237563_s_at EST <NA> 244313_at EST <NA> 237071_at EST <NA> 229934_at EST<NA> 1555311_at EST <NA> 233264_at EST <NA> 239780_at EST <NA> 238405_atEST 3′HEXO 226416_at HISTONE MRNA 3′ END NM_153332 NP_699163EXORIBONUCLEASE 3′HEXO 231852_at ADORA2A 205013_s_at ADENOSINE A2RECEPTOR NM_000675 NP_000666 ANXA3 209369_at ANNEXIN A3 NM_005139NP_005130 ASAHL 214765_s_at N-ACYLSPHINGOSINE NM_014435 NP_055250AMIDOHYDROLASE-LIKE PROTEIN ASAHL 232072_at ASAHL 227135_at ATP11B1554557_at ATPASE, CLASS VI, TYPE 11B XM_087254 XP_087254 ATP6V1C1202872_at ATPASE, H+ TRANSPORTING, NM_001695 NP_001686 LYSOSOMAL, 42-KD,V1 NM_001007254 NP_001007255 SUBUNIT C, ISOFORM 1 B4GALT5 221485_atBETA-1,4- NM_004776 NP_004767 GALACTOSYLTRANSFERASE BASP1 202391_atBRAIN-ABUNDANT SIGNAL NM_006317 NP_006308 PROTEIN BAZ1A 217986_s_atBROMODOMAIN ADJACENT NM_013448 NP_038476 TO ZINC FINGER DOMAIN,NM_182648 NP_872589 1A BCL6 203140_at B-CELL LYMPHOMA 6 NM_001706NP_001697 NM_138931 NP_620309 BMX 206464_at BONE MARROW KINASE, X-NM_001721 NP_001712 LINKED NM_203281 NP_975010 C16orf7 205781_atCHROMOSOME 16 OPEN- NM_004913 NP_004904 READING FRAME 7 C20orf321554786_at CHROMOSOME 20 OPEN- NM_020356 NP_065089 READING FRAME 32 C3F203547_at COMPLEMENT COMPONENT NM_005768 NP_005759 3 C8FW 202241_at C8FWGENE; NM_025195 NP_079471 PHOSPHOPROTEIN. CEACAM1 209498_atCARCINOEMBRYONIC NM_001712 NP_001703 ANTIGEN-RELATED CELL ADHESIONMOLECULE 1 CEACAM1 211883_x_at CECR1 219505_at CAT EYE SYNDROMENM_017424 NP_059120 CHROMOSOME REGION, NM_177405 NP_803124 CANDIDATE 1CHCHD7 222701_s_at COILED-COIL-HELIX NM_001011667 NP_001011667DOMAIN-CONTAINING NM_001011668 NP_001011668 PROTEIN 7 NM_001011669NP_001011669 NM_001011670 NP_001011670 NM_001011671 NP_001011671NM_024300 NP_077276 CHSY1 203044_at CARBOHYDRATE NM_014918 NP_055733SYNTHASE 1 CKLF 223451_s_at CHEMOKINE-LIKE FACTOR NM_016326 NP_057410NM_016951 NP_058647 NM_181640 NP_857591 NM_181641 NP_857592 CKLF219161_s_at CL25022 217883_at C2ORF25 NM_015702 NP_056517 CPD 201940_atCARBOXYPEPTIDASE D NM_001304 NP_001295 CPD 201941_at CRTAP 1554464_a_atCARTILAGE-ASSOCIATED NM_006371 NP_006362 PROTEIN DHRS9 219799_s_atMEMBRANE PROTEIN, NM_005771 NP_005762 PALMITOYLATED 3; MPP3 NM_199204NP_954674 EIF4G3 201936_s_at EUKARYOTIC NM_003760 NP_003751 TRANSLATIONINITIATION FACTOR 4-GAMMA, 3 FAD104 218618_s_at FIBRONECTIN TYPE IIINM_022763 NP_073600 DOMAIN CONTAINING 3B (FNDC3B) FAD104 225032_atFAD104 222692_s_at FAD104 222693_at FCGR1A 214511_x_at FC FRAGMENT OFIGG, HIGH NM_000566 NP_000557 FCGR1A 216950_s_at AFFINITY IA FLJ11175229005_at NM_018349 NP_060819 FLJ11175 220603_s_at FLJ11259 218627_atNM_018370 NP_060840 FLJ11795 220112_at NM_024669 NP_078945 FLJ22833219334_s_at HYPOTHETICAL PROTEIN NM_022837 FLJ22833 G0S2 213524_s_atNM_015714 NP_056529 GADD45B 207574_s_at GROWTH ARREST- AND DNA NM_015675NP_056490 DAMAGE-INDUCIBLE GENE GADD45 GADD45B 209304_x_at GK 214681_atGLYCEROL KINASE NM_000167 NP_000158 NM_203391 NP_976325 GPR160 223423_atG PROTEIN-COUPLED NM_014373 NP_055188 RECEPTOR 160 HLA-DMA 217478_s_atHLA-D NM_006120 NP_006111 HISTOCOMPATIBILITY TYPE HLA-DMB 203932_atNM_002118 NP_002109 HLA-DPA1 211991_s_at NM_033554 NP_291032 HLA-DQB1209823_x_at NM_002123 NP_002114 HLA-DRA 210982_s_at NM_002123 NP_002114HLA-DRA 208894_at HLA-DRB1 215193_x_at NM_002124 NP_002115 HLA-DRB1209312_x_at HLA-DRB4 204670_x_at NM_021983 NP_068818 HLA-DRB4208306_x_at HPGD 203913_s_at 15- NM_000860 NP_000851HYDROXYPROSTAGLANDIN DEHYDROGENASE HYDROXYPROSTAGLANDIN DEHYDROGENASE15-(NAD) HRPT2 218578_at HYPERPARATHYROIDISM 2 NM_024529 NP_078805HSPC163 228437_at HSPC163 PROTEIN NM_014184 NP_054903 HSPC163218728_s_at IDI1 204615_x_at ISOPENTENYL- NM_004508 NP_004499DIPHOSPHATE DELTA- ISOMERASE IL18R1 206618_at INTERLEUKIN 18 RECEPTORNM_003855 NP_003846 1 KCNE1 236407_at POTASSIUM CHANNEL, NM_000219NP_000210 VOLTAGE-GATED, ISK- RELATED SUBFAMILY KIF1B 225878_at KINESINFAMILY MEMBER NM_015074 NP_055889 1B NM_183416 NP_904325 KREMEN1227250_at KRINGLE CONTAINING NM_032045 NP_114434 TRANSMEMBRANE PROTEINNM_153379 NP_700358 1 LDLR 202068_s_at LOW DENSITY LIPOPROTEIN NM_000527NP_000518 RECEPTOR LIMK2 202193_at LIM DOMAIN KINASE 2 NM_005569NP_005560 NM_016733 NP_057952 LOC199675 235568_at HYPOTHETICAL PROTEINNM_174918 NP_777578 LOC199675 LOC284829 225669_at LOC285771 237870_atHYPOTHETICAL PROTEIN LOC285771) LRG1 228648_at LEUCINE-RICH ALPHA-2-NM_052972 NP_443204 GLYCOPROTEIN 1 MGC22805 239196_at NOVEL GENE(MGC22805), MGC22805 238439_at MPEG1 226841_at MACROPHAGE EXPRESSEDXM_166227 XP_166227 GENE 1 OAT 201599_at ORNITHINE NM_000274 NP_000265AMINOTRANSFERASE DEFICIENCY ORF1-FL49 224707_at PUTATIVE NUCLEARNM_032412 NP_115788 PROTEIN ORF1-FL49 PDCD1LG1 227458_at PROGRAMMED CELLDEATH NM_014143 NP_054862 1 LIGAND 1 PFKFB2 226733_at 6-PHOSPHOFRUCTO-2-NM_006212 NP_006203 KINASE PFKFB2 209992_at PFKFB3 202464_s_at6-PHOSPHOFRUCTO-2- NM_004566 NP_004557 KINASE/FRUCTOSE-2,6-BISPHOSPHATASE 3 PGS1 219394_at PHOSPHATIDYLGLYCERO- NM_024419 NP_077733PHOSPHATE SYNTHASE PHTF1 205702_at PUTATIVE HOMEODOMAIN NM_006608NP_006599 TRANSCRIPTION FACTOR 1 PIK3AP1 226459_at PHOSPHOINOSITIDE 3-NM_152309 NP_689522 KINASE ADAPTOR PROTEIN 1 PLSCR1 241916_atPHOSPHOLIPID NM_021105 NP_066928 SCRAMBLASE 1 PRO2852 223797_atHYPOTHETICAL PROTEIN PRO2852 PRV1 219669_at NEUTROPHIL-SPECIFICNM_020406 NP_065139 ANTIGEN 1 (POLYCYTHEMIA RUBRA VERA 1) PSTPIP2219938_s_at PROLINE/SERINE/THREONINE NM_024430 NP_077748 PHOSPHATASE-INTERACTING PROTEIN 1 (PROLINE-SERINE- THREONINE PHOSPHATASE INTERACTINGPROTEIN 2) PTDSR 212723_at CHROMOSOME 17 GENOMIC NM_015167 NP_055982CONTIG, ALTERNATE ASSEMBLY (PHOSPHATIDYLSERINE RECEPTOR) RABGEF1218310_at RAB GUANINE NUCLEOTIDE NM_014504 NP_055319 EXCHANGE FACTOR(RAB GUANINE NUCLEOTIDE EXCHANGE FACTOR (GEF) 1) RARA 228037_at RETINOICACID RECEPTOR, NM_000964 NP_000955 ALPHA RNASEL 229285_at RIBONUCLEASE LNM_021133 NP_066956 SAMSN1 1555638_a_at SAM DOMAIN, SH3 DOMAIN,NM_022136 NP_071419 AND NUCLEAR LOCALIZATION SIGNALS 1 SAMSN1220330_s_at SEC15L1 226259_at SEC15-LIKE 1 NM_019053 NP_061926 (S.CEREVISIAE) (SEC15L1), MRNA SIPA1L2 225056_at SIGNAL-INDUCED NM_020808NP_065859 PROLIFERATION- ASSOCIATED GENE 1 (SIGNAL-INDUCEDPROLIFERATION- ASSOCIATED 1 LIKE 2) SLC26A8 237340_at SOLUTE CARRIERFAMILY NM_052961 NP_443193 26, MEMBER 8 NM_138718 NP_619732 SLC2A3202499_s_at SOLUTE CARRIER FAMILY 2 NM_006931 NP_008862 (FACILITATEDGLUCOSE TRANSPORTER), MEMBER 3 SOCS3 227697_at SUPPRESSOR OF CYTOKINENM_003955 NP_003946 SIGNALING 3 SOD2 216841_s_at SUPEROXIDE DISMUTASE 2NM_000636 NP_000627 SPPL2A 226353_at SIGNAL PEPTIDE NM_032802 NP_116191PEPTIDASE-LIKE 2A SRPK1 202200_s_at PROTEIN KINASE, NM_003137 NP_003128SERINE/ARGININE-SPECIFIC, 1 (SFRS PROTEIN KINASE 1) STK3 204068_atSERINE/THREONINE NM_006281 NP_006272 PROTEIN KINASE 3 SULF2 224724_atSULFATASE 2 (SULF2), NM_198596 NP_940998 TRANSCRIPT VARIANT 2 SULF2233555_s_at T2BP 226117_at TRAF-2 BINDING PROTEIN NM_052864 NP_443096(TRAF-INTERACTING PROTEIN WITH A FORKHEAD-ASSOCIATED DOMAIN) T2BP235971_at T2BP 238858_at TBC1D8 204526_s_at TBC1 DOMAIN FAMILY,NM_007063 NP_008994 MEMBER 8 TCTEL1 201999_s_at T-COMPLEX-ASSOCIATED-NM_006519 NP_006510 TESTIS-EXPRESSED 1 TGFBI 201506_at TRANSFORMINGGROWTH NM_000358 NP_000349 FACTOR, BETA-1 TTYH2 223741_s_at TWEETY,DROSOPHILA, NM_032646 NP_116035 HOMOLOG OF, 2 NM_052869 NP_443101 WDFY3212598_at WD REPEAT AND FYVE NM_014991 NP_055806 DOMAIN CONTAINING 3NM_178583 NP_848698 NM_178585 NP_848700 WDFY3 212606_at WSB1 201296_s_atWD REPEAT AND SOCS BOX- NM_015626 NP_056441 CONTAINING 1 NM_134264NP_599026 NM_134265 NP_599027 ZDHHC19 231122_x_at ZINC FINGER, DHHCNM_144637 NP_653238 DOMAIN CONTAINING 19 (ZDHHC19) ZDHHC19 1553952_atZFP36L2 201367_s_at ZINC FINGER PROTEIN 36, NM_006887 NP_008818 C3HTYPE-LIKE 2; ZFP36L2

Each of the sequences, genes, proteins, and probesets identified inTable 32 is hereby incorporated by reference herein in its entirety.

6.8 Biomarker Combinations Based on Additional Filtering Criteria

Section 6.6 describes exemplary biomarkers that discriminate betweenconverters and nonconverters. Section 6.7 describes one exemplarycombination of the biomarkers of Section 6.6. The biomarkers of Section6.7 were identified by the application of an additional filteringcriterion to the biomarkers of Section 6.6. This section describesadditional combinations of the biomarkers identified in Section 6.7. Thesubsections identified in this section discriminate between convertersand nonconverters.

Table 33 lists biomarkers in one exemplary combination. The combinationdetailed in Table 33 was identified by taking the list of biomarkers inTable 31 and imposing additional filtering criteria. These additionalcriteria include a requirement that each respective biomarker underconsideration exhibit at least a 1.2× fold change between the medianfeature value for the respective biomarker among the subjects thatacquired sepsis during a defined time period (sepsis subjects) and themedian value for the respective biomarker among subjects that do notacquire sepsis during the defined time period (SIRS subjects) in theT⁻¹² baseline data described in Section 6.5. Furthermore, the summationof the PAM score, CART score, and RF score for the biomarker in the T⁻¹²baseline data time period had to exceed unity. Application of theseadditional filtering criteria reduced the biomarkers from the 130 foundin Table 31, to ten biomarkers.

TABLE 33 Exemplary combination of biomarkers that discriminate betweenconverters and non-converters Affymetrix T⁻¹² Values T⁻³⁶ Values GeneProbeset Gene Median Median Symbol name Name FC Direction FC DirectionColumn Column Column Column Column Column Column 1 2 3 4 5 6 71555785_a_at EST 1.34 up 1.31 up CL25022  217883_at 1.36 up 1.31 up IDI1 204615_x_at ISOPENTENYL- 1.51 up 1.31 up DIPHOSPHATE DELTA- ISOMERASEMGC22805  239196_at NOVEL GENE 2.30 up 1.86 up (MGC22805) ORF1- 224707_at 1.75 up 1.65 up FL49 ZDHHC19 1553952_at 1.37 up 1.38 up CHSY1 203044_at CARBOHYDRATE 1.78 up 1.24 up SYNTHASE 1 SLC2A3  202499_s_atSOLUTE 1.87 up 1.71 up CARRIER FAMILY 2 (FACILITATED GLUCOSETRANSPORTER), MEMBER 3 FAD104  225032_at 1.63 up 1.41 up T2BP  235971_at1.47 up 1.27 up

Each of the sequences, genes, proteins, and probesets identified inTable 33 is hereby incorporated by reference.

Table 34 lists biomarkers in yet another exemplary combination ofbiomarkers. The combination detailed in Table 34 was identified bytaking the list of biomarkers in Table 31 and imposing the additionalrequirement that each biomarker is annotated with a corresponding knowngene and that such a gene has a known biological function. Methods,tables, software and other resources for addressing this latter questionare available from the Gene Ontology Consortium, (www.geneontology.org),which is hereby incorporated by reference in its entirety. Applicationof these additional filtering criteria reduced the biomarkers from the130 found in the set of Table 31, to 52 biomarkers, representing 42unique gene sequences (see FIG. 30).

TABLE 34 Exemplary combination of biomarkers that discriminate betweenconverters and non-converters Gene Symbol Corresponding Gene Name (BCL6na) (B-cell CLL/lymphoma 6 (zinc finger protein 51) LOC389185)(HLA-DRB1, 3, 4, 5) (major histocompatibility complex class II DR beta 13, 4, 5) (RABGEF1 na) (LOC401368 LOC402538 RAB guanine nucleotideexchange factor (GEF) 1) 3HEXO 3 exoribonuclease ADORA2A adenosine A2areceptor ANKRD22 ankyrin repeat domain 22 ANXA3 annexin A3 ATP11B ATPaseClass VI type 11B ATP6V1C1 ATPase H+ transporting lysosomal 42 kDa V1subunit C isoform 1 BASP1 brain abundant membrane attached signalprotein 1 BAZ1A bromodomain adjacent to zinc finger domain 1A C16orf7chromosome 16 open reading frame 7 CD4 CD4 antigen (p55) CEACAM1carcinoembryonic antigen-related cell adhesion molecule 1 (biliaryglycoprotein) CECR1 cat eye syndrome chromosome region candidate 1 CKLFchemokine-like factor CPD carboxypeptidase D EIF4G3 eukaryotictranslation initiation factor 4 gamma 3 FCGR1A Fc fragment of IgG highaffinity Ia receptor for (CD64) G0S2 putative lymphocyte G0/G1 switchgene GADD45B growth arrest and DNA-damage-inducible beta HLA-DMB majorhistocompatibility complex class II DM beta HLA-DPA1 majorhistocompatibility complex class II DP alpha 1 HLA-DQB1 majorhistocompatibility complex class II DQ beta 1 HLA-DRA majorhistocompatibility complex class II DR alpha HPGD hydroxyprostaglandindehydrogenase 15-(NAD) IL18R1 interleukin 18 receptor 1 KCNE1 potassiumvoltage-gated channel Isk-related family member 1 LDLR low densitylipoprotein receptor (familial hypercholesterolemia) PDCD1LG1 programmedcell death 1 ligand 1 PHTF1 putative homeodomain transcription factor 1PRV1 polycythemia rubra vera 1 PTDSR phosphatidylserine receptor RARAretinoic acid receptor alpha RNASEL ribonuclease L (25-oligoisoadenylatesynthetase-dependent) SEC15L1 SEC 15-like 1 (S. cerevisiae) SLC26A8solute carrier family 26 member 8 SLC2A3 solute carrier family 2(facilitated glucose transporter) member 3 STK3 serine/threonine kinase3 (STE20 homolog yeast) TGFBI transforming growth factor beta-induced 68kDa XRN1 5-3 exoribonuclease 1 ZFP36L2 zinc finger protein 36 C3Htype-like 2

Each of the sequences, genes, proteins, and probesets identified inTable 34 is hereby incorporated by reference.

In one embodiment, the biomarker profile comprises a plurality ofbiomarkers that collectively contain at least five, at least ten atleast fifteen, at least twenty, at least thirty, between 2 and 5,between 3 and 7, or less than 15 of the sequences of the probesets ofTable 32, or complements thereof, or genes including one of at leastfive of the sequences or complements thereof, or a discriminatingfragment thereof, or an amino acid sequence encoded by any of theforegoing nucleic acid sequences, or any discriminating fragment of suchan amino acid sequence. Such biomarkers can be mRNA transcripts, cDNA orsome other form of amplified nucleic acid or proteins.

In one embodiment, the biomarker profile comprises a plurality ofbiomarkers that collectively contain at least five, at least ten atleast fifteen, at least twenty, at least thirty, between 2 and 5,between 3 and 7, or less than 15 of the sequences of the probesets ofTable 33, or complements thereof, or genes including one of at leastfive of the sequences or complements thereof, or a discriminatingfragment thereof, or an amino acid sequence encoded by any of theforegoing nucleic acid sequences, or any discriminating fragment of suchan amino acid sequence. Such biomarkers can be, for example, mRNAtranscripts, cDNA or some other nucleic acid, for example amplifiednucleic acid, or proteins.

In one embodiment, the biomarker profile comprises a plurality ofbiomarkers that collectively contain at least five, at least ten atleast fifteen, at least twenty, at least thirty, between 2 and 5,between 3 and 7, or less than 15 of the sequences of the probesets ofTable 34, or complements thereof, or genes including one of at leastfive of the sequences or complements thereof, or a discriminatingfragment thereof, or an amino acid sequence encoded by any of theforegoing nucleic acid sequences, or any discriminating fragment of suchan amino acid sequence. Such biomarkers can be, for example, mRNAtranscripts, cDNA or some other nucleic acid, for example amplifiednucleic acid, or proteins.

In one embodiment, the biomarker profile comprises a plurality ofbiomarkers that collectively contain at least five, at least ten atleast fifteen, at least twenty, at least thirty, between 2 and 5,between 3 and 7, or less than 15 of the sequences of the probesets ofTable 33 or Table 34, or complements thereof, or genes including one ofat least five of the sequences or complements thereof, or adiscriminating fragment thereof, or an amino acid sequence encoded byany of the foregoing nucleic acid sequences, or any discriminatingfragment of such an amino acid sequence. Such biomarkers can be, forexample, mRNA transcripts, cDNA or some other nucleic acid, for exampleamplified nucleic acid, or proteins.

In one embodiment, the biomarker profile comprises a biomarker that hasthe sequence of U133 plus 2.0 probeset SLC2A3 or a complement thereof,or a gene including the sequence of the probeset SLC2A3 or a complementthereof, or a discriminating fragment thereof, or an amino acid sequenceencoded by any of the foregoing nucleic acid sequences, or anydiscriminating fragment of such an amino acid sequence. Such biomarkerscan be, for example, mRNA transcripts, cDNA or some other nucleic acid,for example amplified nucleic acid, or proteins.

In the case where a biomarker is based upon a gene that includes thesequence of a probeset listed in Table 30, 31, 32, 33, or 34 or acomplement thereof, the biomarker can be, for example, a transcript madeby the gene, a complement thereof, or a discriminating fragment orcomplement thereof, or a cDNA thereof, or a discriminating fragment ofthe cDNA, or a discriminating amplified nucleic acid moleculecorresponding to all or a portion of the transcript or its complement,or a protein encoded by the gene, or a discriminating fragment of theprotein, or an indication of any of the above. Further still, thebiomarker can be, for example, a protein encoded by a gene that includesa probeset sequence described in Table 30, 31, 32, 33 or 34, or adiscriminating fragment of the protein, or an indication of the above.Here, a discriminating molecule or fragment is a molecule or fragmentthat, when detected, indicates presence or abundance of theabove-identified transcript, cDNA, amplified nucleic acid, or protein.

6.9 Differential Gene Expression in the Th1/Th2 Pathway in SIRS andSepsis Patients

This section describes methods used to identify a set of biomarkers thatdiscriminate between converters and nonconverters, using the methodsdescribed, e.g., in Section 5.10, supra. Briefly, 97 SIRS subject wereadmitted to critical care units of a major university trauma center wereevaluated using the methods described in Section 6.1. Comparisons weremade using T⁻³⁶ and T⁻¹² static data described in Sections 6.3 and 6.4,respectively. The subjects were divided into two classes: converters(47) and nonconverters (50). Blood samples drawn from converters weretime matched to samples from nonconverters in order to performcomparisons, as described in Sections 6.3 and 6.4, respectively. Theblood samples were collected and analyzed as described in Section 6.2.

Biomarkers that discriminated between converters and nonconverters with(i) a Wilcoxon (adjusted) p value of 0.05 or less and (ii) an exhibiteda mean fold differential expression value between converters andnonconverters of 1.2 or greater in either the T⁻³⁶ or the T⁻¹² statictest were selected as the set of discriminating biomarkers. This set ofdiscriminating biomarkers was then filtered using an annotation databased filtering rule imposed by DAVID 2.0, which is available from theNational Institutes of Health (see, http://apps1.niaid.nih.gov/david/,the contents of which are incorporated by reference herein in theirentirety). Specifically, that annotation data based filtering ruleimposed by David 2.0 had the form of annotation rule 4 in Section 5.10,reproduce below

Annotation Rule 4.

Select all biomarkers that are in biological pathway X.The specific form of this annotation data based filtering rule in thisexample wasSelect all biomarkers that are in the Th1/Th2 biological pathway (celldifferentiation pathway).Table 35 below lists the Affymetrix U133 plus 2.0 probesets that are ingenes known to be involved in this Th1/Th2 cell differentiation pathway.

TABLE 35 U133 plus 2.0 Probesets in genes involved in the Th1/Th2 celldifferentiation pathway. U133 + 2.0 P value Symbol probeset (adjusted)CD28 211856_x_at 0.018 CD28 211861_x_at CD28 206545_at CD86 205685_at0.0034 CD86 205685_at 0.0187 CD86 210895_s_at 0.0034 CD86 210895_s_at0.0187 CD86 205686_s_at 0.0114 HLA-DRA 210982_s_at 0.0034 HLA-DRA210982_s_at 0.0187 HLA-DRA 208894_at 0.0034 HLA-DRA 208894_at 0.0187HLA-DRB1 221491_x_at HLA-DRB1 217323_at HLA-DRB1 217362_x_at 0.0034HLA-DRB1 217362_x_at 0.0472 HLA-DRB1 215193_x_at 0.0034 HLA-DRB1215193_x_at 0.0187 HLA-DRB1 209728_at HLA-DRB1 204670_x_at 0.0034HLA-DRB1 204670_x_at 0.0187 HLA-DRB1 208306_x_at 0.0034 HLA-DRB1208306_x_at 0.0187 HLA-DRB1 209312_x_at 0.0034 HLA-DRB1 209312_x_at0.0187 HLA-DRB1 215666_at HLA-DRB1 215669_at HLA-DRB3 221491_x_atHLA-DRB3 217323_at HLA-DRB3 217362_x_at 0.0034 HLA-DRB3 217362_x_at0.0472 HLA-DRB3 215193_x_at 0.0034 HLA-DRB3 215193_x_at 0.0187 HLA-DRB3209728_at HLA-DRB3 204670_x_at 0.0034 HLA-DRB3 204670_x_at 0.0187HLA-DRB3 208306_x_at 0.0034 HLA-DRB3 208306_x_at 0.0187 HLA-DRB3209312_x_at 0.0034 HLA-DRB3 209312_x_at 0.0187 HLA-DRB3 215666_atHLA-DRB3 215669_at HLA-DRB4 221491_x_at HLA-DRB4 217323_at HLA-DRB4217362_x_at 0.0034 HLA-DRB4 217362_x_at 0.0472 HLA-DRB4 215193_x_at0.0034 HLA-DRB4 215193_x_at 0.0187 HLA-DRB4 209728_at HLA-DRB4204670_x_at 0.0034 HLA-DRB4 204670_x_at 0.0187 HLA-DRB4 208306_x_at0.0034 HLA-DRB4 208306_x_at 0.0187 HLA-DRB4 209312_x_at 0.0034 HLA-DRB4209312_x_at 0.0187 HLA-DRB4 215666_at HLA-DRB4 215669_at HLA-DRB5221491_x at HLA-DRB5 217323_at HLA-DRB5 217362_x_at 0.0034 HLA-DRB5217362_x_at 0.0472 HLA-DRB5 215193_x_at 0.0034 HLA-DRB5 215193_x_at0.0187 HLA-DRB5 209728_at HLA-DRB5 204670_x_at 0.0034 HLA-DRB5204670_x_at 0.0187 HLA-DRB5 208306_x_at 0.0034 HLA-DRB5 208306_x_at0.0187 HLA-DRB5 209312_x_at 0.0034 HLA-DRB5 209312_x_at 0.0187 HLA-DRB5215666_at HLA-DRB5 215669_at IFNG 210354_at IFNGR1 211676_s_at 0.00342IFNGR1 242903_at 0.0034 IFNGR1 202727_s_at 0.0054 IFNGR2 231696_x_atIFNGR2 201642_at 0.0034 IL12A 207160_at IL12B 207901_at IL12RB1239522_at IL12RB1 206890_at IL12RB2 206999_at IL18 206295_at IL18R1206618_at 0.0034 IL18R1 206618_at 0.0187 IL2 207849_at IL2RA 211269_s_atIL2RA 206341_at 0.0247 IL4 207538_at IL4 207539_s_at IL4R 203233_at0.0034 IL4R 203233_at 0.0187 TNFRSF5 222292_at 0.0126 TNFRSF5 215346_atTNFRSF5 205153_s_at TNFRSF5 35150_at 0.0086 TNFSF5 207892_at 0.0034

Table 36 below identifies the genes that contain the probesets thatremained in the set of discriminating biomarkers upon application of theannotation data based filtering rule.

TABLE 36 Identified genes. Data Fold-change source Adjusted (Mediansepsis vs. Relative Gene name (static) p value Median SIRS) regulationCD86 T⁻¹² 0.003 1.56 Down T⁻³⁶ 0.019 1.23 Down HLA-DRA T⁻¹² 0.003 1.29Down T⁻³⁶ 0.019 1.23 Down HLA-DRB1,3,4,5 T⁻¹² 0.003 1.25 Down T⁻³⁶ 0.0191.27 Down IFNGR1 T⁻¹² 0.003 1.39 Up IFNGR2 T⁻¹² 0.003 1.25 Up IL18R1T⁻¹² 0.003 3.17 Up T⁻³⁶ 0.019 2.37 Up IL4R T⁻¹² 0.003 1.61 Up T⁻³⁶ 0.0191.39 Up

The genes in Table 36 represent biomarkers that discriminate betweenconverters and converters. Further, these genes are in the Th1/Th2 celldifferentiation pathway. The results in the table show that, althoughclinically similar, SIRS patients who subsequently developed sepsisexpressed genes related to Th1/Th2 Cells differently than SIRS patientswho remained uninfected. These differences occurred prior to the onsetof clinical sepsis. For a discussion of Th1/Th2 cell differentiationpathway genes and related genes, see, e.g., Abbas et al., 1996,Functional diversity of helper T lymphocytes, Nature 383:787-793; Fearonand Locksley, 1996, Science 272:50-53; and Mossman and Sad, 1996,Immunol. Today 17:138-146; each of which is hereby incorporated byreference in its entirety.

6.10 RT-PCR

In Section 6.1, it was noted that two Paxgene (RNA) tubes were drawnfrom each subject in the study on each day of the study. One tube wasused for microarray analysis as described in Section 6.2. The other tubewas used for RT-PCR analysis. In this section, the correlation betweenthe gene expression values obtained by RT-PCR and the gene expressionvalues obtained by microarray is presented for three of the genes listedin Table 30, IL18R1, FCGR1A, and MMP9. In this comparison, staticexpression data from both assays (RT-PCR and microarray) for all timepoints measured in the subject were correlated to obtain a correlationcoefficient. The correlations were computed within ‘R’, a public domainstatistical computing language (http://www.r-project.org/, which ishereby incorporated by reference), using the following code:

  corCalc <- function(x,y){  n <- length(x)  ## if there are any missingvalues in the data  if(any(is.na(x)) ∥ any(is.na(y))){   ## index wheremissing values occur   rm.idx <- which(is.na(x))   rm.idx <- c(rm.idx,which(is.na(y)))   ## remove missing values   x <- x[-rm.idx]   y <-y[-rm.idx]   ## update length   n <- length(x)  }  R <-(n*sum(x*y)-sum(x)*sum(y))/(sqrt((n*sum(x{circumflex over ( )}2)-(sum(x)){circumflex over ( )}2)*(n*sum(y{circumflex over ( )}2) -(sum(y)){circumflex over ( )}2)))  return(R) }

FIG. 31 shows the correlation between IL18R1 expression, as determinedby RT-PCR, and the intensity of the X206618_at probeset, as determinedusing the techniques described in Section 6.2, using all available timepoints across the training population. Each point in FIG. 31 is the geneexpression value for a given subject in the training population from theRT-PCR data and the microarray data. Substantial correlation between theRT-PCR and the microarray data was found. In particular, the overallcorrelation between expression of IL18R1 as determined by RT-PCR andmicroarray data for X206618_at was 0.85.

FIG. 32 shows the correlation between FCGR1A expression, as determinedby RT-PCR, and the intensity of the X214511_x_at, X216950_s_at andX216951_at probesets, as determined using the techniques described inSection 6.2, using all available time points in the training population.Each point in FIG. 32 is the gene expression value for a given subjectin the training population from the RT-PCR data and the microarray data.As is evident in FIG. 32, the overall correlation between expression ofFCGR1A and each of the two FCGR1A probesets that are found in Table 30,X214511_x_at and X216950_s_at, was significant. In particular thecorrelation coefficient between FCGR1A and X214511_x_at was 0.88.Likewise, the correlation coefficient between and FCGR1A andX216950_s_at was 0.88. The overall correlation between expression ofFCGR1A and the FCGR1A probeset not found in Table 30, X216951_at, was0.53, which was not as significant as the other two probesets.

FIG. 33 shows the correlation between MMSP9 expression, as determined byRT-PCR, and the intensity of the X203936_s_at probeset, as determinedusing the techniques described in Section 6.2, using all available timepoints in the training population. Each point in FIG. 32 is the geneexpression value for a given subject in the study from the RT-PCR dataand the microarray data. Substantial correlation between the RT-PCR andthe microarray data was found. In particular, the overall correlationbetween expression of MMP9 as determined by RT-PCR and microarray datafor X203936_s_at was 0.87.

FIG. 34 shows the correlation between CD86 expression, as determined byRT-PCR, and the intensity of the X205685_at, X205686_s_at, andX210895_s_at probesets, as determined using the techniques described inSection 6.2, using all available time points. Each point in FIG. 34 isthe gene expression value for a given subject in the study from theRT-PCR data and the microarray data. As is evident in FIG. 34, theoverall correlation between expression of CD86 and CD86 probeset that isfound in Table 30, 210895_s_at, was significant (correlation coefficientof 0.71). The overall correlation between expression of CD86 and theprobesets not found in Table 30, X205685_at, X205686_s_at, was not assignificant (correlation coefficient of 0.66 and 0.56, respectively).

In one embodiment, a biomarker profile of the present inventioncomprises a plurality of biomarkers selected from Table 30, including atleast one sequence of a probeset in the set of:

{X206618_at, X214511_x_at, X216950_s_at, X203936_s_at, and 210895_s_at}

or complements thereof, or genes including the sequence or a complementof the sequence thereof, or a discriminating fragment thereof, or anamino acid sequence encoded by any of the foregoing nucleic acidsequences, or any discriminating fragment of such an amino acidsequence. Such biomarkers can be, for example, mRNA transcripts, cDNA orsome other nucleic acid, for example, amplified nucleic acid orproteins. In one embodiment, a biomarker profile the of the presentinvention comprises a nucleic acid that codes for TGFB1, IL18R1, orFCGR1A, a discriminating portion of TGFB1, IL18R1, or FCGR1A,complements of such nucleic acids, proteins encoded by such nucleicacids, or antibodies that selectively bind to any of the foregoing.

6.11 Discovery of Select Nucleic Acid Biomarkers

The experiments described above identified a number of biomarkers thatdiscriminate between sepsis and SIRS. In this example, a discoveryprocess was performed in order to confirm which biomarkers differentiatebetween patients who subsequently develop sepsis (“sepsis patients”) andpatients who do not (“SIRS patients”). In the discovery process, samplesfrom SIRS patients and sepsis patients taken at: (i) date of entry, (ii)T⁻⁶⁰, (iii) T⁻³⁶, and (iv) T⁻¹² data points were studied by RT-PCR, asdescribed in Section 6.11.1 and by Affymetrix gene chip analysis, asdescribed in Section 6.11.2.

6.11.1 RT-PCR Analysis

Biomarkers in multiple samples were measured by RT-PCR at multiple timepoints and analyzed in several different ways: static time of entry,static T⁻⁶⁰, static T⁻³⁶, baseline T⁻⁶⁰, baseline T⁻³⁶, and baselineT⁻¹² data points. RT-PCR is described in Section 5.4.1.2, and 6.10,above. Representative of these analyses is the static T⁻¹² data analysiswhich is described in detail below. In the T⁻¹² static analysis,biomarkers features were measured using a specific blood sample,designated the T⁻¹² blood sample, as defined in Section 6.4, above.

For the T⁻¹² static analysis, there were 72 biomarkers measured on 96samples. Each sample was collected from a different member thepopulation. Of these features, 15 were transformed by logtransformations, 5 by square root transformations and the remaining 52were not transformed.

The 96 member population was initially split into a training set (n=73)and a validation set (n=23). The training set was used to estimate theappropriate classification algorithm parameters while the trainedalgorithm was applied to the validation set to independently assessperformance. Of the 73 training samples, 36 were labeled Sepsis, meaningthat the subjects developed sepsis at some point during the observationtime period, and 37 were SIRS, meaning that they did not develop sepsisduring the observation time period. Table 37 provides distributions ofthe race, gender and age for these samples.

TABLE 37 Distributions of the race, gender, and age for the trainingdata Group Gender Black Caucasian Other Sepsis Male 10 14 1 Female 0 101 SIRS Male 5 22 0 Female 0 10 0 Group Minimum Mean Median MaximumSepsis 18 43.2 40 80 SIRS 18 44.6 40 90

For the 23 validation samples, 12 were labeled Sepsis and 11 werelabeled SIRS. Table 38 provides distributions of the race, gender andage for these samples.

TABLE 38 Distributions of the race, gender, and age for the validationdata Group Gender Black Caucasian Other Sepsis Male 0 7 0 Female 0 4 0SIRS Male 2 6 0 Female 0 4 0 Group Minimum Mean Median Maximum Sepsis 1843.4 43 81 SIRS 19 51.9 51.5 85

Each sample in the training data was randomly assigned to one of tengroups used for cross-validation. The number of training samples inthese groups ranged from 6 to 8. The samples were assigned in way thatattempted to balance the number of sepsis and SIRS samples across folds.As described in more detail below, several different methods were usedto judge whether select biomarkers discriminate between the Sepsis andSIRS groups.

Wilcoxon and Q-Value Tests.

The first method used to identify discriminating biomarkers was aWilcoxon test (unadjusted). The abundance value for a given biomarkeracross the samples in the training data was subjected to the Wilcoxontest. The Wilcoxon test considers both group classification (sepsisversus SIRS) and abundance value in order to compute a p value for thegiven biomarker. The p value provides an indication of how well theabundance value for the given biomarker across the samples collected inthe training set discriminates between the sepsis and SIRS state. Thelower the p value, the better the discrimination. When the p value isless than a specific confidence level, such as 0.05, an inference ismade that the biomarker discriminates between the sepsis and SIRSphenotype. There were 33 significant biomarkers using this method (seeTable 39).

The second method used to identify discriminating biomarkers was theWilcoxon Test (adjusted). Due to the large number of biomarkers, 72, andthe relatively small number of samples, 96, there was a high risk offinding falsely significant biomarkers. An adjusted p-value was used tocounter this risk. In particular, the method of Benjamini and Hochberg,1995, J.R. Statist. Soc. B 57, pp 289-300, which is hereby incorporatedherein by reference in its entirety, was used to control the falsediscovery rate. Here, the false discovery rate is defined as the numberof biomarkers truly significant divided by the number of biomarkersdeclared significant. For example, if the adjusted p-value is less than0.05, there is a 5% chance that the biomarker is a false discovery.Results using this test are reported in Table 39. There were 11851significant biomarkers using this method (see Table 39). As used,herein, a biomarker is considered significant if it has a p-value ofless than 0.05 as determined by the Wilcoxon test (adjusted).

The third method used to identify discriminating biomarkers was the useof Q values. In such an approach, the biomarkers are ordered by theirq-values and if a respective biomarker has a Q value of X, thenrespective biomarker and all others more significant have a combinedfalse discovery rate of X. However, the false discovery rate for any onebiomarker may be much larger. There were 27 significant biomarkers usingthis method (see Table 39).

TABLE 39 Cumulative number of significant calls for the three methods.Note that all samples (training and validation) were used to compareSepsis and SIRS groups. Missing biomarker feature values were notincluded in the analyses. ≦1e−04 ≦0.001 ≦0.01 ≦0.025 ≦0.05 ≦0.1 ≦1p-value 0 22 25 29 33 38 72 (unadjusted) p-value 0 16 25 25 27 33 72(adjusted) q-value 0 0 28 38 47 59 72

CART.

In addition to analyzing the microarray data using Wilcoxon and Q-valuetests in order to identify biomarkers that discriminate between thesepsis and SIRS subpopulations in the training set, classification andregression tree (CART) analysis was used. CART is described in Section5.5.1, above. Specifically, the data summarized above was used topredict the disease state by iteratively partitioning the data based onthe best single-variable split of the data. In other words, at eachstage of the tree building process, the biomarker whose expressionvalues across the training population best discriminate between thesepsis and SIRS population was invoked as a decision branch.Cross-validation was carried out, with the optimal number of splitsestimated independently in each of the 10 iterations. The final tree isdepicted in FIG. 36, and uses seven biomarkers: TNFSF13B, FCGR1A, HMOX1,MMP9, APAF1, APAF1.1, and CCL3.

FIG. 37 shows the distribution of the seven biomarkers used in thedecision tree between the sepsis and SIRS groups in the training dataset. In FIG. 37, the top of each box denotes the 75^(th) percentile ofthe data across the training set and the bottom of each box denotes the25^(th) percentile, and the median value for each biomarker across thetraining set is drawn as a line within each box. The confusion matrixfor the training data where the predicted classifications were made fromthe cross-validated model is given in Table 40. From this confusionmatrix, the overall accuracy was estimated to be 68.5% with a 95%confidence interval of 56.6% to 78.9%. The estimated sensitivity was72.2% and the estimated specificity was 64.9%.

TABLE 40 Confusion matrix for training samples using the cross-validatedCART algorithm of FIG. 36 True Diagnosis Predicted Sepsis SIRS Sepsis 2613 SIRS 10 24

For the 23 validation samples held back from training data set, theoverall accuracy was estimated to be 78.3% with a 95% confidenceinterval of 56.3% to 92.5%, sensitivity 66.7% and specificity 90.9%.Table 41 shows the confusion matrix for the validation samples.

TABLE 41 Confusion matrix for validation samples using thecross-validated CART algorithm True Diagnosis Predicted Sepsis SIRSSepsis 8 1 SIRS 4 10

Random Forests.

Another decision rule that can be developed using biomarkers of thepresent invention is a Random Forests decision tree. Random Forests is atree based method that uses bootstrapping instead of cross-validation.For each iteration, a random sample (with replacement) is drawn and thelargest tree possible is grown. Each tree receives a vote in the finalclass prediction. To fit a random forest, the number of trees (e.g.bootstrap iterations) is specified. No more than 500 were used in thisexample, but at least 50 are needed for a burn-in period. The number oftrees was chosen based on the accuracy of the training data. For thisdata, 462 trees were used to train the algorithm (see FIG. 38). In FIG.38, curve 3202 is a smoothed estimate of overall accuracy as a functionof tree number. Curve 3804 is a smoothed curve of tree sensitivity as afunction of tree number. Curve 3806 is a smoothed curve of treespecificity as a function of tree number. Using this algorithm, 49biomarkers had non-zero importance and were used in the model. Therandom forest algorithm gauges biomarker importance by the averagereduction in the training accuracy. The biomarkers were ranked by thismethod and are shown in FIG. 39. The random forest method uses a numberof different decision trees. A biomarker is considered to havediscriminating significance if it served as a decision branch of adecision tree from a significant random forest analysis. As used herein,a significant random forest analysis is one where the lower 95%confidence interval on accuracy by cross validation on a training dataset is greater than 50% and the point estimate for accuracy on avalidation set is greater than 65%.

The predicted confusion matrix for the training dataset using thedecision tree developed using the Random Forest method is given in Table42. From this confusion matrix, the overall accuracy was estimated to be76.7% (confidence intervals cannot be computed when using the bootstrapaccuracy estimate). The estimated sensitivity was 77.8% and theestimated specificity was 75.7%.

TABLE 42 Confusion matrix for training samples against the decision treedeveloped using the Random Forest method. True Diagnosis PredictedSepsis SIRS Sepsis 28 8 SIRS 9 28

For the 23 validation samples held back from training, the overallaccuracy was estimated to be 78.3% with a 95% confidence interval of56.3% to 92.5%, sensitivity 75% and specificity 81.8%. Table 43 showsthe confusion matrix for the validation samples.

TABLE 43 Confusion matrix for the validation samples against thedecision tree developed using the Random Forest method. True DiagnosisPredicted Sepsis SIRS Sepsis 9 2 SIRS 3 9

MART.

Multiple Additive Regression Trees (MART), also known as “gradientboosting machines,” was used to simultaneously assess the importance ofbiomarkers and classify the subject samples. Several fitting parametersare specified in this approach including (i) number of trees, (ii) stepsize (commonly referred to as “shrinkage”), and (iii) degree ofinteraction (related to the number of splits for each tree). Moreinformation on MART is described in Section 5.5.4 above. The degree ofinteraction was set to 1 to enforce an additive model (e.g. each treehas one split and only uses one biomarker).

Estimating interactions may require more data to function well. The stepsize was set to 0.05 so that the model complexity was dictated by thenumber of trees. The optimal number of trees was estimated by leavingout a random subset of cases at each fitting iteration, then assessingquality of prediction on that subset. After fitting more trees than werewarranted, the point at which prediction performance stopped improvingwas estimated as the optimal point.

The estimated model used 15 trees and 6 biomarkers across all trees. TheMART algorithm also provides a calculation of biomarker importance(summing to 100%), which are given in FIG. 40. Biomarkers with zeroimportance were excluded. FIG. 41 shows the distribution of the selectedbiomarkers between the Sepsis and SIRS groups.

Cross-validation was carried out, with the optimal number of treesestimated independently in each of the 10 iterations. The confusionmatrix for the training data where the predicted classifications weremade from the cross-validated model is given in Table 44. From thisconfusion matrix, the overall accuracy was estimated to be 75.3% with a95% confidence interval of 63.9% to 84.7%. The estimated sensitivity was72.2% and the estimated specificity was 78.4%.

TABLE 44 Confusion matrix for the training samples using thecross-validated MART algorithm. True Diagnosis Predicted Sepsis SIRSSepsis 26 8 SIRS 10 29

For the 23 validation samples held back from training, the overallaccuracy was estimated to be 78.3% with a 95% confidence interval of56.3% to 92.5%, sensitivity 81.8% and specificity 75%. Table 45 showsthe confusion matrix for the validation samples.

TABLE 45 Confusion matrix for the validation samples using the MARTalgorithm. True Diagnosis Predicted Sepsis SIRS Sepsis 9 3 SIRS 2 9

PAM.

Yet another decision rule developed using biomarkers of the presentinvention is predictive analysis of microarrays (PAM), which isdescribed in Section 5.5.2, above. In this method, a shrinkage parameterthat determines the number of biomarkers used to classify samples isspecified. This parameter was chosen via cross-validation. There were nobiomarkers with missing values. Based on cross-validation, the optimalthreshold value was 2.12, corresponding to 5 biomarkers. FIG. 42 showsthe accuracy across different thresholds. In FIG. 42, curve 4202 is theoverall accuracy (with 95% confidence interval bars). Curve 4204 showsdecision rule sensitivity as a function of threshold value. Curve 4206shows decision rule specificity as a function of threshold value. Usingthe threshold of 2.12, the overall accuracy for the training samples wasestimated to be 80.8% with a 95% confidence interval of 70.9% to 87.9%.The estimated sensitivity was 89.2% and the estimated specificity was72.2%. Table 46 shows the confusion matrix for the training data wherethe predicted classifications were made from the cross-validated models.

TABLE 46 Confusion matrix for training samples using cross-validated PAMalgorithm True Diagnosis Predicted Sepsis SIRS Sepsis 33 10 SIRS 4 26

For the 23 validation samples held back from training, the overallaccuracy was estimated to be 82.6% with a 95% confidence interval of61.2% to 95%, sensitivity 91.7% and specificity 72.7%. Table 47 showsthe confusion matrix for the validation samples.

TABLE 47 Confusion matrix for validation samples using cross-validatedPAM algorithm True Diagnosis Predicted Sepsis SIRS Sepsis 11 3 SIRS 1 8

FIG. 43 shows the selected biomarkers, ranked by their relativediscriminatory power, and their relative importance in the model.

FIG. 44 provides a summary of the CART, MART, PAM, and random forests(RF) classification algorithm (decision rule) performance and associated95% confidence intervals. Fifty distinct biomarkers were selected fromacross all the algorithms illustrated in FIG. 44. The identity of thesefifty selected features is found in FIG. 45, which further illustratesan overall ranking of these biomarkers for the T⁻¹² data set. For theselected biomarkers, the x-axis depicts the percentage of times that itwas selected. Within the percentage of times that biomarkers wereselected, the biomarkers are ranked.

From the analysis of the T⁻¹² data set and the other data sets,biomarkers were ranked according to how often they were included in theCART, MART, PAM, random forests. The results of this ranking issummarized in Table 48 below:

TABLE 48 Top ranked biomarkers as determined by RT-PCR Gene Protein GeneAccession Accession T⁻¹² symbol Gene Name Number Number Hours T⁻³⁶ HoursFCGR1A FC FRAGMENT OF IGG, HIGH NM_000566 NP_000557 1 1 AFFINITY IA MMP9MATRIX NM_004994 NP_004985 2 5 METALLOPROTEINASE 9 (GELATINASE B, 92 KDAGELATINASE, 92 KDA TYPE IV COLLAGENASE) IL18R1 INTERLEUKIN 18 RECEPTOR 1NM_003855 NP_003846 3 2 ARG2 ARGINASE TYPE II NM_001172 CAG38787 4 3IL1RN INTERLEUKIN-1 RECEPTOR NM_000577, AAN87150 4 4 ANTAGONIST GENENM_173841, NM_173842, NM_173843 TNFSF13B TUMOR NECROSIS FACTOR NM_006573NP_006564 4 5 (LIGAND) SUPERFAMILY, MEMBER 13B ITGAM INTEGRIN, ALPHA MNM_000632 NP_000623 5 7 (COMPLEMENT COMPONENT RECEPTOR 3, ALPHA; ALSOKNOWN AS CD11B (P170), MACROPHAGE ANTIGEN ALPHA POLYPEPTIDE) CD4 CD4ANTIGEN (P55) NM_000616 NP_000607 6 7 TGFBI TRANSFORMING GROWTHNM_000358 NP_000349 6 9 FACTOR, BETA-1 (TRANSFORMING GROWTH FACTOR,BETA-INDUCED, 68 KDA) CD86 CD86 ANTIGEN (CD28 NM_006889 NP_008820 6 6ANTIGEN LIGAND 2, B7-2 NM_175862 NP_787058 ANTIGEN) TLR4 TOLL-LIKERECEPTOR 4 AH009665 AAF05316 6 6 IFI16 INTERFRON, GAMMA- NM_005531AAH17059 6 9 INDUCIBLE PROTEIN 16 ICAM1 INTERCELLULAR ADHESION NM_010493NP_000192, 6 10 MOLECULE 1 AAQ14902, AAQ14901 TGFBR2 TRANSFORMING GROWTHNM_003242 AAF27281 6 8 FACTOR, BETA RECEPTOR II PLA2G7PLATELET-ACTIVATING NM_005084 CAH73907 7 6 FACTOR ACETYLHYDROLASE IL-10INTERLEUKIN 10 NM_000572 CAH73907 8 7

As Table 48 indicates, in general, important biomarkers at T⁻¹² werealso important biomarkers at earlier time points. The ten biomarkersthat are italicized in Table 48 were carried forward to confirmation asdescribed in Section 6.12.1, below. CD4 was excluded in this embodimentbecause it was found to be different on day of entry.

6.11.2 Discovery Affymetrix Gene Chip Analysis

The patients were also analyzed using Affymetrix gene chip analysis.Such an analysis is described in Section 6.2 Biomarkers in multiplesamples were measured by Affymetrix gene chip analysis at multiple timepoints and analyzed in several different ways: static time of entry,static T⁻⁶⁰, static T⁻³⁶, baseline T⁻⁶⁰, baseline T⁻³⁶, and baselineT⁻¹² data points. The Affymetrix gene chip assay is described in Section6.2, above. Representative of these analyses is the static T⁻¹² dataanalysis described in detail below. In the T⁻¹² static analysis,biomarkers features were measured using a specific blood sample,designated the T⁻¹² blood sample, as defined in Section 6.4, above.

For the T⁻¹² static analysis, there were 54,613 biomarkers measured on90 samples. Each sample was collected from a different member thepopulation. Of these features, 31,047 were transformed by logtransformations, 2518 by square root transformations and the remaining21,048 were not transformed.

The 90 member population was initially split into a training set (n=69)and a validation set (n=21). The training set was used to estimate theappropriate classification algorithm parameters while the trainedalgorithm was applied to the validation set to independently assessperformance. Of the 69 training samples, 34 were labeled Sepsis, meaningthat the subjects developed sepsis at some point during the observationtime period, and 35 were SIRS, meaning that they did not develop sepsisduring the observation time period. Table 49 provides distributions ofthe race, gender and age for these samples.

TABLE 49 Distributions of the race, gender, and age for the trainingdata Group Gender Black Caucasian Other Sepsis Male 9 13 1 Female 0 10 1SIRS Male 5 20 0 Female 0 10 0 Group Minimum Mean Median Maximum Sepsis18 44.1 39 80 SIRS 18 44.1 40 90

For the 21 validation samples, 11 were labeled Sepsis and 10 werelabeled SIRS. Table 50 provides distributions of the race, gender andage for these samples.

TABLE 50 Distributions of the race, gender, and age for the validationdata Group Gender Black Caucasian Other Sepsis Male 0 7 0 Female 0 3 0SIRS Male 2 6 0 Female 0 3 0 Group Minimum Mean Median Maximum Sepsis 1843.4 40 81 SIRS 19 53 52 85

Each sample in the training data was randomly assigned to one of tengroups used for cross-validation. The number of training samples inthese groups ranged from 6 to 8. The samples were assigned in way thatattempted to balance the number of sepsis and SIRS samples across folds.As described in more detail below, several different methods were usedto judge whether select biomarkers discriminate between the Sepsis andSIRS groups.

Wilcoxon and Q-Value Tests.

The first method used to identify discriminating biomarkers was aWilcoxon test (unadjusted). The abundance value for a given biomarkeracross the samples in the training data was subjected to the Wilcoxontest. The Wilcoxon test considers both group classification (sepsisversus SIRS) and abundance value in order to compute a p value for thegiven biomarker. The p value provides an indication of how well theabundance value for the given biomarker across the samples collected inthe training set discriminates between the sepsis and SIRS state. Thelower the p value, the better the discrimination. When the p value isless than a specific confidence level, such as 0.05, an inference ismade that the biomarker discriminates between the sepsis and SIRSphenotype. There were 19791 significant biomarkers using this method(see Table 51).

The second method used to identify discriminating biomarkers was theWilcoxon Test (adjusted). Due to the large number of biomarkers, 54613,and the relatively small number of samples, 90, there was a high risk offinding falsely significant biomarkers. An adjusted p-value was used tocounter this risk. In particular, the method of Benjamini and Hochberg,1995, J.R. Statist. Soc. B 57, pp 289-300, which is hereby incorporatedherein by reference in its entirety, was used to control the falsediscovery rate. Here, the false discovery rate is defined as the numberof biomarkers truly significant divided by the number of biomarkersdeclared significant. For example, if the adjusted p value is less than0.05, there is a 5% chance that the biomarker is a false discovery.Results using this test are reported in Table 51. There were 11851significant biomarkers using this method (see Table 51). As used,herein, a biomarker is considered significant if it has a p-value ofless than 0.05 as determined by the Wilcoxon test (adjusted).

The third method used to identify discriminating biomarkers was the useof Q values. In such an approach, the biomarkers are ordered by theirq-values and if a respective biomarker has a Q value of X, thenrespective biomarker and all others more significant have a combinedfalse discovery rate of X. However, the false discovery rate for any onebiomarker may be much larger. There were 11581 significant biomarkersusing this method (see Table 51).

TABLE 51 Cumulative number of significant calls for the three methods.Note that all 96 samples (training and validation) were used to compareSepsis and SIRS groups. Missing biomarker feature values were notincluded in the analyses. ≦1e−04 ≦0.001 ≦0.01 ≦0.025 ≦0.05 ≦0.1 ≦1p-value 0 5417 11537 15769 19791 24809 54613 (unadjusted) p-value 0 05043 8374 11851 16973 54613 (adjusted) q-value 0 0 7734 12478 1782024890 54613

CART.

In addition to analyzing the microarray data using Wilcoxon and Q-valuetests in order to identify biomarkers that discriminate between thesepsis and SIRS subpopulations in the training set, classification andregression tree (CART) analysis was used. CART is described in Section5.5.1, above. Specifically, the data summarized above was used topredict the disease state by iteratively partitioning the data based onthe best single-variable split of the data. In other words, at eachstage of the tree building process, the biomarker whose expressionvalues across the training population best discriminate between thesepsis and SIRS population was invoked as a decision branch.Cross-validation was carried out, with the optimal number of splitsestimated independently in each of the 10 iterations. The final treeuses four probesets: X214681_at, X230281_at, X1007_s_at, andX1560432_at, where each given probeset is the U133 plus 2.0 Affymetrixprobe set name. The confusion matrix for the training data, based on thefinal tree from the cross-validated CART algorithm is given in Table 52.From this confusion matrix, the overall accuracy was estimated to be65.2% with a 95% confidence interval of 52.8% to 76.3%. The estimatedsensitivity was 61.8% and the estimated specificity was 68.6%.

TABLE 52 Confusion matrix for training samples using the cross-validatedCART algorithm True Diagnosis Predicted Sepsis SIRS Sepsis 21 11 SIRS 1324

For the 21 validation samples held back from training data set, theoverall accuracy was estimated to be 71.4% with a 95% confidenceinterval of 47.8% to 88.7%, sensitivity 90.9% and specificity 50%. Theconfusion matrix for the validation samples was predicted Sepsis/trueSepsis 10, predicted SIRS/true Sepis 1, predicted Sepsis/true SIRS 5,predicted SIRS/true SIRS 5.

Random Forests.

Another decision rule that can be developed using biomarkers of thepresent invention is a Random Forests decision tree. Random Forests is atree based method that uses bootstrapping instead of cross-validation.For each iteration, a random sample (with replacement) is drawn and thelargest tree possible is grown. Each tree receives a vote in the finalclass prediction. To fit a random forest, the number of trees (e.g.bootstrap iterations) is specified. No more than 500 were used in thisexample, but at least 50 are needed for a burn-in period. The number oftrees was chosen based on the accuracy of the training data. For thisdata, 439 trees were used to train the algorithm. Using this algorithm,845 biomarkers had non-zero importance and were used in the model. Therandom forest algorithm gauges biomarker importance by the averagereduction in the training accuracy.

The random forest method uses a number of different decision trees. Abiomarker is considered to have discriminating significance if it servedas a decision branch of a decision tree from a significant random forestanalysis. As used herein, a significant random forest analysis is onewhere the lower 95% confidence interval on accuracy by cross validationon a training data set is greater than 50% and the point estimate foraccuracy on a validation set is greater than 65%.

The predicted confusion matrix for the training dataset using thedecision tree developed using the Random Forest method is given in Table53. From this confusion matrix, the overall accuracy was estimated to be75.4% (confidence intervals cannot be computed when using the bootstrapaccuracy estimate). The estimated sensitivity was 73.5% and theestimated specificity was 77.1%.

TABLE 53 Confusion matrix for training samples against the decision treedeveloped using the Random Forest method. True Diagnosis PredictedSepsis SIRS Sepsis 27 9 SIRS 8 25

For the 21 validation samples held back from training, the overallaccuracy was estimated to be 76.2% with a 95% confidence interval of76.2% to 99.9%, sensitivity 100% and specificity 90%. Table 54 shows theconfusion matrix for the validation samples.

TABLE 54 Confusion matrix for the validation samples against thedecision tree developed using the Random Forest method. True DiagnosisPredicted Sepsis SIRS Sepsis 11 1 SIRS 0 9

MART.

Multiple Additive Regression Trees (MART), also known as “gradientboosting machines,” was used to simultaneously assess the importance ofbiomarkers and classify the subject samples. Several fitting parametersare specified in this approach including (i) number of trees, (ii) stepsize (commonly referred to as “shrinkage”), and (iii) degree ofinteraction (related to the number of splits for each tree). Moreinformation on MART is described in Section 5.5.4 above. The degree ofinteraction was set to 1 to enforce an additive model (e.g. each treehas one split and only uses one biomarker).

Estimating interactions may require more data to function well. The stepsize was set to 0.05 so that the model complexity was dictated by thenumber of trees. The optimal number of trees was estimated by leavingout a random subset of cases at each fitting iteration, then assessingquality of prediction on that subset. After fitting more trees than werewarranted, the point at which prediction performance stopped improvingwas estimated as the optimal point.

The estimated model used 28 trees and 17 biomarkers across all trees.The MART algorithm also provides a calculation of biomarker importance(summing to 100%). Biomarkers ranked in decreasing order of importanceto the model, with the most important biomarker first were: X206513_at,X214681_at, X235359_at, X221850_x_at, X213524_s_at, X225656_a,X200881_s_at, X229743_at, X215178_x_at, X215178_x_at, X216841_s_at,X216841_at, X244158_at, X238858_at, X205287_s_at, X233651_s_at,X229572_at, X214765_s_at.

Cross-validation was carried out, with the optimal number of treesestimated independently in each of the 10 iterations. The confusionmatrix for the training data where the predicted classifications weremade from the cross-validated model is given in Table 55. From thisconfusion matrix, the overall accuracy was estimated to be 76.8% with a95% confidence interval of 65.1% to 86.1%. The estimated sensitivity was76.5% and the estimated specificity was 77.1%.

TABLE 55 Confusion matrix for the training samples using thecross-validated MART algorithm. True Diagnosis Predicted Sepsis SIRSSepsis 26 8 SIRS 8 27

For the 21 validation samples held back from training, the overallaccuracy was estimated to be 85.7% with a 95% confidence interval of63.7% to 97%, sensitivity 80% and specificity 90.9%. Table 56 shows theconfusion matrix for the validation samples.

TABLE 56 Confusion matrix for the validation samples using the MARTalgorithm. True Diagnosis Predicted Sepsis SIRS Sepsis 8 1 SIRS 2 10

PAM.

Yet another decision rule developed using biomarkers of the presentinvention is predictive analysis of microarrays (PAM), which isdescribed in Section 5.5.2, above. In this method, a shrinkage parameterthat determines the number of biomarkers used to classify samples isspecified. This parameter was chosen via cross-validation. There were nobiomarkers with missing values. Based on cross-validation, the optimalthreshold value was 2.1, corresponding to 820 biomarkers.

Using the threshold of 2.1, the overall accuracy for the trainingsamples was estimated to be 80.9% with a 95% confidence interval of73.4% to 86.7%. The estimated sensitivity was 85.7% and the estimatedspecificity was 76.5%. Table 57 shows the confusion matrix for thetraining data where the predicted classifications were made from thecross-validated models.

TABLE 57 Confusion matrix for training samples using cross-validated PAMalgorithm True Diagnosis Predicted Sepsis SIRS Sepsis 11 1 SIRS 0 9

For the 21 validation samples held back from training, the overallaccuracy was estimated to be 95.2% with a 95% confidence interval of76.2% to 99.9%, sensitivity 100% and specificity 90%. Table 58 shows theconfusion matrix for the validation samples.

TABLE 58 Confusion matrix for validation samples using cross-validatedPAM algorithm True Diagnosis Predicted Sepsis SIRS Sepsis 11 1 SIRS 0 9

The top ten biomarkers identified by PAM, ranked from most important toleast important were: X206513_at, X213524_s_at, X200881_s_at,X218992_at, X238858_at, X221123_x_at, X228402_at, X230585_at,X209304_x_at, X214681_at.

FIG. 46 provides a summary of the CART, MART, PAM, and random forests(RF) classification algorithm (decision rule) performance and associated95% confidence intervals using T⁻¹² static data obtained from anAffymetrix gene chip discovery training population. Fifty distinctbiomarkers were selected from across all the algorithms illustrated inFIG. 46. The identity of the top 50 biomarkers, ranked from most toleast significant is: X204102_s_at, X236013_at, X213668_s_at,X1556639_at, X218220_at, X207860_at, X232422_at, X218578_at,X205875_s_at, X226043_at, X225879_at, X224618_at, X216316_x_at,X243159_x_at, X202200_s_at, X201936_s_at, X242492_at, X216609_at,X214328_s_at, X228648_at, X223797_at, X225622_at, X205988_at,X201978_s_at, X200874_s_at, X210105_s_at, X203913_s_at, X204225_at,X227587_at, X220865_s_at, X206682_at, X222664_at, X212264_s_at,X219669_at, X221971_x_at, X1554464_a_at, X242590_at, X227925_at,X221926_s_at, X202101_s_at, X211078_s_at, X44563_at, X206513_at,X215178_x_at, X235359_at, X225656_at, X244158_at, X214765_s_at,X229743_at, X214681.

From the analysis of the T⁻¹² data set and the other data sets, the 34biomarkers indicated in Table 59 below were selected for confirmation.As indicated in Table 59, biomarkers were selected based on theAffymetrix gene chip analysis for one of three criteria, biologicalrelevance (BR), high fold change (HF), or statistical importance (SI) inthe Affymetrix gene chip analysis.

TABLE 59 Nucleic acid based biomarkers selected for confirmation fromAffymetrix Assay Gene Protein Accession Accession Selection Gene SymbolGene Name Number Number Criterion BCL2A1 BCL2-RELATED PROTEIN NM_004049NP_004040 BR A1 CCL5 CHEMOKINE (C-C MOTIF) NM_002985 NP_002976 BR LIGAND5 CSF1R COLONY STIMULATING NM_005211 NP_005202 BR FACTOR 1 RECEPTOR,FORMERLY MCDONOUGH FELINE SARCOMA VIRAL (V-FMS) ONCOGENE HOMOLOG GADD45AGROWTH ARREST AND NM_001924 NP_001915 BR DNA-DAMAGE- INDUCIBLE, ALPHAGADD45B GROWTH ARREST- AND NM_015675 NP_056490 BR DNA DAMAGE- INDUCIBLEGENE GADD45 IFNGR1 INTERFERON GAMMA NM_000416 NP_000407 BR RECEPTOR 1IL10RA INTERLEUKIN 10 NM_001558 NP_001549 BR RECEPTOR, ALPHA IRAK2INTERLEUKIN-1 NM_001570 NP_001561 BR RECEPTOR-ASSOCIATED KINASE 2 IRAK4INTERLEUKIN-1 NM_016123 NP_057207 BR RECEPTOR-ASSOCIATED KINASE 4 JAK2JANUS KINASE 2 (A NM_004972 NP_004963 BR PROTEIN TYROSINE KINASE) LY96LYMPHOCYTE ANTIGEN NM_015364 NP_056179 BR 96 MAP2K6 MITOGEN-ACTIVATEDNM_002758 NP_002749 BR PROTEIN KINASE KINASE 6 NM_031988 NP_114365MAPK14 MAPK14 MITOGEN- NM_001315 NP_001306 BR ACTIVATED PROTEINNM_139012 NP_620581 KINASE 14 NM_139013 NP_620582 NM_139014 NP_620583MKNK1 MAP KINASE NM_003684 NP_003675 BR INTERACTING NM_198973 NP_945324SERINE/THREONINE KINASE 1 OSM ONCOSTATIN M NM_020530 NP_065391 BR SOCS3SUPPRESSOR OF NM_003955 NP_003946 BR CYTOKINE SIGNALING 3 TDRD9 TUDORDOMAIN NM_153046 NP_694591 BR CONTAINING 9 TNFRSF6 TUMOR NECROSISNM_152877 NP_000034 BR FACTOR RECEPTOR SUPERFAMILY, MEMBER 6 TNFSF10TUMOR NECROSIS NM_003810 NP_003801 BR FACTOR (LIGAND) SUPERFAMILY,MEMBER 10 ANKRD22 ANKYRIN REPEAT NM_144590 NP_653191 HF DOMAIN 22 ANXA3ANNEXIN A3 NM_005139 NP_005130 HF CEACAM1 CARCINOEMBRYONIC NM_001712NP_001703 HF ANTIGEN-RELATED CELL ADHESION MOLECULE 1 LDLR LOW DENSITYNM_000527 NP_000518 HF LIPOPROTEIN RECEPTOR PFKFB3 6-PHOSPHOFRUCTO-2-NM_004566 NP_004557 HF KINASE/FRUCTOSE-2,6- BISPHOSPHATASE 3 PRV1NEUTROPHIL-SPECIFIC NM_020406 NP_065139 HF ANTIGEN 1 (POLYCYTHEMIA RUBRAVERA 1) PSTPIP2 PROLINE/SERINE/THREONINE NM_024430 NP_077748 HFPHOSPHATASE- INTERACTING PROTEIN 1 (PROLINE-SERINE- THREONINEPHOSPHATASE INTERACTING PROTEIN 2) TIFA TRAF-INTERACTING NM_052864NP_443096 HF PROTEIN WITH A FORKHEAD-ASSOCIATED DOMAIN VNN1 VANIN 1NM_004666 NP004657 HF NCR1 NATURAL NM_004829 NP_004820 SI CYTOTOXICITYTRIGGERING RECEPTOR 1 FAD104 FIBRONECTIN TYPE III NM_022763 NP_073600 SIDOMAIN CONTAINING 3B (FNDC3B) INSL3 INSULIN-LIKE 3 (LEYDIG NM_005543NP_005534 SI CELL) CRTAP CARTILAGE-ASSOCIATED NM_006371 NP_006362 SIPROTEIN HLA-DRA MAJOR NM_002123 NP_002114 SI HISTOCOMPATIBILITY COMPLEX,CLASS II, DR ALPHA SOD2 SUPEROXIDE DISMUTASE NM_000636 NP_000627 SI 2,MITOCHONDRIAL

6.12 Confirmation of Select Nucleic Acid Biomarkers

In this example, a confirmatory process was performed in order toconfirm which biomarkers differentiate between patients who subsequentlydevelop sepsis (“sepsis patients”) and patients who do not (“SIRSpatients”).

6.12.1 Confirmatory Analysis of Biomarkers Identified by RT-PCR

The biomarkers identified by italicizes in Table 48 of Section 6.11.1,namely FCGR1A, MMP9, IL18R1, ARG2, IL1RN, TNFSF13B, ITGAM, TGFBI, CD86,and TLR4, were analyzed using RT-PCR at multiple time points andanalyzed in several different ways: static time of entry, static T⁻⁶⁰,static T⁻³⁶, baseline T⁻⁶⁰, baseline T⁻³⁶, and baseline T⁻¹² datapoints. RT-PCR is described in Section 5.4.1.2, above. Representative ofthese analyses is the static T⁻¹² data analysis described in detailbelow. In the T⁻¹² static analysis, biomarkers features were measuredusing a specific blood sample, designated the T⁻¹² blood sample, asdefined in Section 6.4, above.

For the T⁻¹² static analysis, the biomarkers FCGR1A, MMP9, IL18R1, ARG2,IL1RN, TNFSF13B, ITGAM, TGFBI, CD86, and TLR4, were measured from 50samples. Each sample was collected from a different member of thepopulation. Of these biomarkers, seven were transformed by logtransformations, and three by square root transformations.

The 50 member population was initially split into a training set (n=39)and a validation set (n=11). The training set was used to estimate theappropriate classification algorithm parameters while the trainedalgorithm was applied to the validation set to independently assessperformance. Of the 50 training samples, 23 were labeled Sepsis, meaningthat the subjects developed sepsis at some point during the observationtime period, and 16 were SIRS, meaning that they did not develop sepsisduring the observation time period. Table 60 provides distributions ofthe race, gender and age for these samples.

TABLE 60 Distributions of the race, gender, and age for the trainingdata Group Gender Black Caucasian Other Sepsis Male 3 13 0 Female 0 7 0SIRS Male 5 7 1 Female 0 2 0 Group Minimum Mean Median Maximum Sepsis 2052.3 56 80 SIRS 20 39.9 32.5 79

For the 11 validation samples, five were labeled Sepsis and six werelabeled SIRS. Table 61 provides distributions of the race, gender andage for these samples.

TABLE 61 Distributions of the race, gender, and age for the validationdata Group Gender Black Caucasian Other Sepsis Male 2 1 0 Female 0 3 0SIRS Male 0 3 0 Female 0 2 0 Group Minimum Mean Median Maximum Sepsis 1851.7 59.5 76 SIRS 24 47.2 43 76

Each sample in the training data was randomly assigned to one of tengroups used for cross-validation. The number of training samples inthese groups ranged from three to five. The samples were assigned in waythat attempted to balance the number of sepsis and SIRS samples acrossfolds. As described in more detail below, several different methods wereused to judge whether select biomarkers discriminate between the Sepsisand SIRS groups.

Wilcoxon and Q-Value Tests.

The first method used to identify discriminating biomarkers was aWilcoxon test (unadjusted). The abundance value for a given biomarkeracross the samples in the training data was subjected to the Wilcoxontest. The Wilcoxon test considers both group classification (sepsisversus SIRS) and abundance value in order to compute a p value for thegiven biomarker. The p value provides an indication of how well theabundance value for the given biomarker across the samples collected inthe training set discriminates between the sepsis and SIRS state. Thelower the p value, the better the discrimination. When the p value isless than a specific confidence level, such as 0.05, an inference ismade that the biomarker discriminates between the sepsis and SIRSphenotype. There were nine significant biomarkers using this method (seeTable 62).

The second method used to identify discriminating biomarkers was theWilcoxon Test (adjusted). Due to the large number of biomarkers, 10, andthe relatively small number of samples, 50, there was a high risk offinding falsely significant biomarkers. An adjusted p value was used tocounter this risk. In particular, the method of Benjamini and Hochberg,1995, J.R. Statist. Soc. B 57, pp 289-300, which is hereby incorporatedherein by reference in its entirety, was used to control the falsediscovery rate. Here, the false discovery rate is defined as the numberof biomarkers truly significant divided by the number of biomarkersdeclared significant. For example, if the adjusted p value is less than0.05, there is a 5% chance that the biomarker is a false discovery.Results using this test are reported in Table 62. There were ninesignificant biomarkers using this method (see Table 62). As used,herein, a biomarker is considered significant if it has a p value ofless than 0.05 as determined by the Wilcoxon test (adjusted).

The third method used to identify discriminating biomarkers was the useof Q values. In such an approach, the biomarkers are ordered by their Qvalues and if a respective biomarker has a Q value of X, then respectivebiomarker and all others more significant have a combined falsediscovery rate of X. However, the false discovery rate for any onebiomarker may be much larger. There were nine significant biomarkersusing this method (see Table 62).

TABLE 62 Cumulative number of significant calls for the three methods.Note that all samples (training and validation) were used to compareSepsis and SIRS groups. Missing biomarker feature values were notincluded in the analyses. ≦1e−04 ≦0.001 ≦0.01 ≦0.025 ≦0.05 ≦0.1 ≦1p-value 0 7 9 9 9 9 10 (unadjusted) p-value 0 7 9 9 9 9 10 (adjusted)q-value 0 0 0 0 0 0 10

CART.

In addition to analyzing the microarray data using Wilcoxon and Q-valuetests in order to identify biomarkers that discriminate between thesepsis and SIRS subpopulations in the training set, classification andregression tree (CART) analysis was used. CART is described in Section5.5.1, above. Specifically, the data summarized above was used topredict the disease state by iteratively partitioning the data based onthe best single-variable split of the data. In other words, at eachstage of the tree building process, the biomarker whose expressionvalues across the training population best discriminate between thesepsis and SIRS population was invoked as a decision branch.Cross-validation was carried out, with the optimal number of splitsestimated independently in each of the 10 iterations. The final treeuses three biomarkers which are listed in order of importance IL18R1,ARG2, and FCGR1A, where IL18R1 was the most important. The confusionmatrix for the training data, based on the final tree from thecross-validated CART algorithm is given in Table 63. From this confusionmatrix, the overall accuracy was estimated to be 82.1% with a 95%confidence interval of 66.5% to 92.5%. The estimated sensitivity was82.6% and the estimated specificity was 81.2%.

TABLE 63 Confusion matrix for training samples using the cross-validatedCART algorithm True Diagnosis Predicted Sepsis SIRS Sepsis 19 3 SIRS 413

For the 11 validation samples held back from training data set, theoverall accuracy was estimated to be 100% with a 95% confidence intervalof 71.5% to 100%, sensitivity 100% and specificity 100%. Table 64 showsthe confusion matrix for the validation samples.

TABLE 64 Confusion matrix for validation samples using thecross-validated CART algorithm True Diagnosis Predicted Sepsis SIRSSepsis 5 0 SIRS 0 6

Random Forests.

Another decision rule that can be developed using biomarkers of thepresent invention is a Random Forests decision tree. Random Forests is atree based method that uses bootstrapping instead of cross-validation.For each iteration, a random sample (with replacement) is drawn and thelargest tree possible is grown. Each tree receives a vote in the finalclass prediction. To fit a random forest, the number of trees (e.g.bootstrap iterations) is specified. For this data, 1000 trees were usedto train the algorithm. Using this algorithm, 9 of the 10 biomarkers hadnon-zero importance and were used in the model. Biomarker importance,from greatest to smallest, was: TGFB1, MMP9, TLR4, IL1RN, TNFSF, ARG2,FCGR1A, and IL18R1.

The random forest method uses a number of different decision trees. Abiomarker is considered to have discriminating significance if it servedas a decision branch of a decision tree from a significant random forestanalysis. As used herein, a significant random forest analysis is onewhere the lower 95% confidence interval on accuracy by cross validationon a training data set is greater than 50% and the point estimate foraccuracy on a validation set is greater than 65%.

The predicted confusion matrix for the training dataset using thedecision tree developed using the Random Forest method is given in Table65. From this confusion matrix, the overall accuracy was estimated to be79.5% with a 95% confidence interval between 63.5% and 90.7%. Theestimated sensitivity was 87% and the estimated specificity was 68.8%.

TABLE 65 Confusion matrix for training samples against the decision treedeveloped using the Random Forest method. True Diagnosis PredictedSepsis SIRS Sepsis 20 5 SIRS 3 11

For the 11 validation samples held back from training, the overallaccuracy was estimated to be 81.8% with a 95% confidence interval of48.2% to 97.7%, sensitivity 60% and specificity 100%. Table 66 shows theconfusion matrix for the validation samples.

TABLE 66 Confusion matrix for the 11 validation samples against thedecision tree developed using the Random Forest method. True DiagnosisPredicted Sepsis SIRS Sepsis 6 0 SIRS 2 3

MART.

Multiple Additive Regression Trees (MART), also known as “gradientboosting machines,” was used to simultaneously assess the importance ofbiomarkers and classify the subject samples. Several fitting parametersare specified in this approach including (i) number of trees, (ii) stepsize (commonly referred to as “shrinkage”), and (iii) degree ofinteraction (related to the number of splits for each tree). Moreinformation on MART is described in Section 5.5.4 above. The degree ofinteraction was set to 1 to enforce an additive model (e.g. each treehas one split and only uses one biomarker).

Estimating interactions may require more data to function well. The stepsize was set to 0.05 so that the model complexity was dictated by thenumber of trees. The optimal number of trees was estimated by leavingout a random subset of cases at each fitting iteration, then assessingquality of prediction on that subset. After fitting more trees than werewarranted, the point at which prediction performance stopped improvingwas estimated as the optimal point.

The estimated model used 30 trees and 7 biomarkers across all trees. TheMART algorithm also provides a calculation of biomarker importance(summing to 100%). Biomarkers ranked in decreasing order of importanceto the model, with the most important biomarker first were: ITGAM,TGFB1, TLR4, TNFSF, FCGR1A, IL18R1, and ARG2.

Cross-validation was carried out, with the optimal number of treesestimated independently in each of the 10 iterations. The confusionmatrix for the training data where the predicted classifications weremade from the cross-validated model is given in Table 67. From thisconfusion matrix, the overall accuracy was estimated to be 74.4% with a95% confidence interval of 57.9% to 87%. The estimated sensitivity was73.8% and the estimated specificity was 68.8%.

TABLE 67 Confusion matrix for the training samples using thecross-validated MART algorithm. True Diagnosis Predicted Sepsis SIRSSepsis 18 5 SIRS 5 11

For the 11 validation samples held back from training, the overallaccuracy was estimated to be 74.4% with a 95% confidence interval of57.9% to 87%, sensitivity 78.3% and specificity 68.8%. Table 68 showsthe confusion matrix for the validation samples.

TABLE 68 Confusion matrix for the validation samples using the MARTalgorithm. True Diagnosis Predicted Sepsis SIRS Sepsis 6 2 SIRS 0 3

PAM.

Yet another decision rule developed using biomarkers of the presentinvention is predictive analysis of microarrays (PAM), which isdescribed in Section 5.5.2, above. In this method, a shrinkage parameterthat determines the number of biomarkers used to classify samples isspecified. This parameter was chosen via cross-validation. There were nobiomarkers with missing values. Based on cross-validation, the optimalthreshold value was 0.55, corresponding to 9 biomarkers.

Using the threshold of 0.55, the overall accuracy for the trainingsamples was estimated to be 82.3% with a 95% confidence interval of68.8% to 90.7%. The estimated sensitivity was 68.8% and the estimatedspecificity was 91.3%. Table 69 shows the confusion matrix for thetraining data where the predicted classifications were made from thecross-validated models.

TABLE 69 Confusion matrix for training samples using cross-validated PAMalgorithm True Diagnosis Predicted Sepsis SIRS Sepsis 11 2 SIRS 5 21

For the 11 validation samples held back from training, the overallaccuracy was estimated to be 72.67% with a 95% confidence interval of39% to 94%, sensitivity 40% and specificity 100%. Table 70 shows theconfusion matrix for the validation samples.

TABLE 70 Confusion matrix for validation samples using cross-validatedPAM algorithm True Diagnosis Predicted Sepsis SIRS Sepsis 2 0 SIRS 3 6

The top nine biomarkers identified by PAM, ranked from most important toleast important were: ARG2, TGFB1, MMP9, TLR4, ITGAM, IL18R1, TNFSF,IL1RN, and FCGR1A. FIG. 47 provides a summary of the CART, MART, PAM,and random forests (RF) classification algorithm (decision rule)performance and associated 95% confidence intervals using T⁻¹² staticdata obtained from an Affymetrix gene chip confirmatory trainingpopulation. Based on the results of the RT-PCR analysis summarized inFIG. 47 and at other time points, all ten biomarkers under study in thisconfirmation process were significant. Some of the biomarkersdiscriminated as early as T⁻⁶⁰.

6.12.2 Confirmatory Analysis of Biomarkers Identified by Affymetrix GeneChip Analysis

The biomarkers identified in Table 59 of Section 6.11.2 and the tenbiomarkers identified in Table 48 of Section 6.11.1 (FCGR1A, MMP9,IL18R1, ARG2, IL1RN, TNFSF13B, ITGAM, TGFBI, CD86, and TLR4), a total of44 biomarkers, were analyzed using RT-PCR at multiple time points andanalyzed in several different ways: static time of entry, static T⁻⁶⁰,static T⁻³⁶, baseline T⁻⁶⁰, baseline T⁻³⁶, and baseline T⁻¹² datapoints. RT-PCR is described in Section 5.4.1.2, above. Representative ofthese analyses is the static T⁻¹² data analysis described in detailbelow. In the T⁻¹² static analysis, biomarkers features were measuredusing a specific blood sample, designated the T⁻¹² blood sample, asdefined in Section 6.4, above.

For the T⁻¹² static analysis, the 44 biomarkers were measured from 37samples. Each sample was collected from a different member of thepopulation. Of these biomarkers, 23 were transformed by logtransformations, and 21 by square root transformations.

The 37 member population was initially split into a training set (n=28)and a validation set (n=9). The training set was used to estimate theappropriate classification algorithm parameters while the trainedalgorithm was applied to the validation set to independently assessperformance. Of the 28 training samples, 14 were labeled Sepsis, meaningthat the subjects developed sepsis at some point during the observationtime period, and 14 were SIRS, meaning that they did not develop sepsisduring the observation time period. Table 71 provides distributions ofthe race, gender and age for these samples.

TABLE 71 Distributions of the race, gender, and age for the trainingdata Group Gender Black Caucasian Other Sepsis Male 1 7 0 Female 0 6 0SIRS Male 4 6 1 Female 0 2 0 Group Minimum Mean Median Maximum Sepsis 2858 56 76 SIRS 20 42.5 39.5 79

For the 9 validation samples, five were labeled Sepsis and four werelabeled SIRS. Table 72 provides distributions of the race, gender andage for these samples.

TABLE 72 Distributions of the race, gender, and age for the validationdata Group Gender Black Caucasian Other Sepsis Male 2 0 0 Female 0 3 0SIRS Male 0 2 0 Female 0 2 0 Group Minimum Mean Median Maximum Sepsis 1849.8 58 76 SIRS 24 45.8 41.5 76

Each sample in the training data was randomly assigned to one of tengroups used for cross-validation. The number of training samples inthese groups ranged from two to four. The samples were assigned in waythat attempted to balance the number of sepsis and SIRS samples acrossfolds. As described in more detail below, several different methods wereused to judge whether select biomarkers discriminate between the Sepsisand SIRS groups.

Wilcoxon and Q-Value Tests.

The first method used to identify discriminating biomarkers was aWilcoxon test (unadjusted). The abundance value for a given biomarkeracross the samples in the training data was subjected to the Wilcoxontest. The Wilcoxon test considers both group classification (sepsisversus SIRS) and abundance value in order to compute a p value for thegiven biomarker. The p value provides an indication of how well theabundance value for the given biomarker across the samples collected inthe training set discriminates between the sepsis and SIRS state. Thelower the p value, the better the discrimination. When the p value isless than a specific confidence level, such as 0.05, an inference ismade that the biomarker discriminates between the sepsis and SIRSphenotype. There were 38 significant biomarkers using this method (seeTable 73).

The second method used to identify discriminating biomarkers was theWilcoxon Test (adjusted). Due to the large number of biomarkers, 44, andthe relatively small number of samples, 37, there was a high risk offinding falsely significant biomarkers. An adjusted p value was used tocounter this risk. In particular, the method of Benjamini and Hochberg,1995, J.R. Statist. Soc. B 57, pp 289-300, which is hereby incorporatedherein by reference in its entirety, was used to control the falsediscovery rate. Here, the false discovery rate is defined as the numberof biomarkers truly significant divided by the number of biomarkersdeclared significant. For example, if the adjusted p value is less than0.05, there is a five percent chance that the biomarker is a falsediscovery. Results using this test are reported in Table 73. There were38 significant biomarkers using this method (see Table 73). As used,herein, a biomarker is considered significant if it has a p value ofless than 0.05 as determined by the Wilcoxon test (adjusted).

The third method used to identify discriminating biomarkers was Qvalues. In this third approach, the biomarkers were ordered by their Qvalues and if a respective biomarker has a Q value of X, then respectivebiomarker and all others more significant have a combined falsediscovery rate of X. However, the false discovery rate for any onebiomarker may be much larger. There were 38 significant biomarkers usingthis method (see Table 73).

TABLE 73 Cumulative number of significant calls for the three methods.Note that all samples (training and validation) were used to comparesepsis and SIRS groups. Missing biomarker feature values were notincluded in the analyses. ≦1e−04 ≦0.001 ≦0.01 ≦0.025 ≦0.05 ≦0.1 ≦1p-value 0 27 38 38 38 38 44 (unadjusted) p-value 0 23 38 38 38 38 44(adjusted) q-value 0 36 38 39 39 39 44

CART.

In addition to analyzing the microarray data using Wilcoxon and Q-valuetests in order to identify biomarkers that discriminate between thesepsis and SIRS subpopulations in the training set, classification andregression tree (CART) analysis was used. CART is described in Section5.5.1, above. Specifically, the data summarized above was used topredict the disease state by iteratively partitioning the data based onthe best single-variable split of the data. In other words, at eachstage of the tree building process, the biomarker whose expressionvalues across the training population best discriminate between thesepsis and SIRS population was invoked as a decision branch.Cross-validation was carried out with the optimal number of splitsestimated independently in each of the 10 iterations. The final treeuses three biomarkers which are listed in order of importance OSM,HLA-DRA, and IL-18, where OSM was the most important. The confusionmatrix for the training data, based on the final tree from thecross-validated CART algorithm is given in Table 74. From this confusionmatrix, the overall accuracy was estimated to be 67.9% with a 95%confidence interval of 47.6% to 84.1%. The estimated sensitivity was64.3% and the estimated specificity was 71.4%.

TABLE 74 Confusion matrix for training samples using the cross-validatedCART algorithm True Diagnosis Predicted Sepsis SIRS Sepsis 9 4 SIRS 5 10

For the 9 validation samples held back from training data set, theoverall accuracy was estimated to be 88.9% with a 95% confidenceinterval of 51.8% to 99.7%, sensitivity 75% and specificity 100%. Table75 shows the confusion matrix for the validation samples.

TABLE 75 Confusion matrix for validation samples using thecross-validated CART algorithm True Diagnosis Predicted Sepsis SIRSSepsis 3 0 SIRS 1 5

Random Forests.

Another decision rule that can be developed using biomarkers of thepresent invention is a Random Forests decision tree. Random Forests is atree based method that uses bootstrapping instead of cross-validation.For each iteration, a random sample (with replacement) is drawn and thelargest tree possible is grown. Each tree receives a vote in the finalclass prediction. To fit a random forest, the number of trees (e.g.bootstrap iterations) is specified. For this data, 1000 trees were usedto train the algorithm. Using this algorithm, 35 of the 44 biomarkershad non-zero importance and were used in the model. Biomarkerimportance, from greatest to smallest, was: OSM, GADD45B, ARG2, IL18R1,TDRD9, PFKFB3, MAPK14, PRV1, MAP2K6, TNFRSF6, FCGR1A, INSL3, LY96,PSTPIP2, ANKRD22, TNFSF10, HLA-DRA, FNDC3B, TIFA, GADD45A, VNN1, ITGAM,BCL2A1, TLR4, TNFSF13B, SOCS3, IL1RN, CEACAM1, and SOD2.

The random forest method uses a number of different decision trees. Abiomarker is considered to have discriminating significance if it servedas a decision branch of a decision tree from a significant random forestanalysis. As used herein, a significant random forest analysis is onewhere the lower 95% confidence interval on accuracy by cross validationon a training data set is greater than 50% and the point estimate foraccuracy on a validation set is greater than 65%.

The predicted confusion matrix for the training dataset using thedecision tree developed using the Random Forest method is given in Table76. From this confusion matrix, the overall accuracy was estimated to be78.6%. The estimated sensitivity was 78.6% and the estimated specificitywas also 78.6%.

TABLE 76 Confusion matrix for training samples against the decision treedeveloped using the Random Forest method. True Diagnosis PredictedSepsis SIRS Sepsis 11 3 SIRS 3 11

For the 9 validation samples held back from training, the overallaccuracy was estimated to be 77.8% with a 95% confidence interval of40.0% to 97.2%, sensitivity 50% and specificity 100%. Table 77 shows theconfusion matrix for the validation samples.

TABLE 77 Confusion matrix for validation samples against the decisiontree developed using the Random Forest method. True Diagnosis PredictedSepsis SIRS Sepsis 5 0 SIRS 2 2

MART.

Multiple Additive Regression Trees (MART), also known as “gradientboosting machines,” was used to simultaneously assess the importance ofbiomarkers and classify the subject samples. Several fitting parametersare specified in this approach including (i) number of trees, (ii) stepsize (commonly referred to as “shrinkage”), and (iii) degree ofinteraction (related to the number of splits for each tree). Moreinformation on MART is described in Section 5.5.4 above. The degree ofinteraction was set to 1 to enforce an additive model (e.g. each treehas one split and only uses one biomarker).

Estimating interactions may require more data to function well. The stepsize was set to 0.05 so that the model complexity was dictated by thenumber of trees. The optimal number of trees was estimated by leavingout a random subset of cases at each fitting iteration, then assessingquality of prediction on that subset. After fitting more trees than werewarranted, the point at which prediction performance stopped improvingwas estimated as the optimal point.

The estimated model used 21 trees and 9 biomarkers across all trees. TheMART algorithm also provides a calculation of biomarker importance(summing to 100%). Biomarkers ranked in decreasing order of importanceto the model, with the most important biomarker first were: ARG2,GADD45B, OSM, LY96, INSL3, ANKRD22, MAP2K6, PSTPIP2, and TGFB1.

Cross-validation was carried out, with the optimal number of treesestimated independently in each of the 10 iterations. The confusionmatrix for the training data where the predicted classifications weremade from the cross-validated model is given in Table 78. From thisconfusion matrix, the overall accuracy was estimated to be 75% with a95% confidence interval of 55.1 to 89.3%. The estimated sensitivity was71.4% and the estimated specificity was 78.6%.

TABLE 78 Confusion matrix for the training samples using thecross-validated MART algorithm. True Diagnosis Predicted Sepsis SIRSSepsis 10 3 SIRS 4 11

For the 9 validation samples held back from training, the overallaccuracy was estimated to be 88.9% with a 95% confidence interval of51.8% to 99.7%, sensitivity 100% and specificity 75%. Table 79 shows theconfusion matrix for the validation samples.

TABLE 79 Confusion matrix for the validation samples using the MARTalgorithm. True Diagnosis Predicted Sepsis SIRS Sepsis 5 1 SIRS 0 3

PAM.

Yet another decision rule developed using biomarkers of the presentinvention is predictive analysis of microarrays (PAM), which isdescribed in Section 5.5.2, above. In this method, a shrinkage parameterthat determines the number of biomarkers used to classify samples isspecified. This parameter was chosen via cross-validation. There were nobiomarkers with missing values. Based on cross-validation, the optimalthreshold value was 2.05, corresponding to 6 biomarkers.

Using the threshold of 2.05, the overall accuracy for the trainingsamples was estimated to be 82.5% with a 95% confidence interval of68.7% to 91%. The estimated sensitivity was 78.6% and the estimatedspecificity was 85.7%. Table 80 shows the confusion matrix for thetraining data where the predicted classifications were made from thecross-validated models.

TABLE 80 Confusion matrix for training samples using cross-validated PAMalgorithm True Diagnosis Predicted Sepsis SIRS Sepsis 11 2 SIRS 3 12

For the 9 validation samples held back from training, the overallaccuracy was estimated to be 77.8% with a 95% confidence interval of 40%to 97.2%, sensitivity 50% and specificity 100%. Table 81 shows theconfusion matrix for the validation samples.

TABLE 81 Confusion matrix for validation samples using cross-validatedPAM algorithm True Diagnosis Predicted Sepsis SIRS Sepsis 2 0 SIRS 2 5

The top six biomarkers identified by PAM, ranked from most important toleast important were: GADD45B, TDRD9, MAP2K6, OSM, TNFSF10, and ANKRD22.FIG. 48 provides a summary of the CART, MART, PAM, and random forests(RF) classification algorithm (decision rule) performance and associated95% confidence intervals using T⁻¹² static data for the 44 biomarkersanalyzed in this Section. Based on the results of the RT-PCR analysissummarized in FIG. 48 and at other time points, all forty-fourbiomarkers under study in this confirmation process were significant.Some of the biomarkers discriminated as early as T⁻⁶⁰.

6.13 Select Protein Biomarkers

In this example, experiments were performed in order to confirm whichprotein based biomarkers differentiate between patients who subsequentlydevelop sepsis (“sepsis patients) and patients who do not (“SIRSpatients). In the discovery process, samples were analyzed by a beadbased protein immunoassay, as described in Section 6.13.1.

6.13.1 Discovery of Protein Biomarkers Using a Bead Based ProteinImmunoassay

Multiplex Analysis.

A set of biomarkers was analyzed simultaneously in real time, using amultiplex analysis method described in U.S. Pat. No. 5,981,180 (“the'180 patent”), herein incorporated by reference in its entirety, and inparticular for its teachings of the general methodology, beadtechnology, system hardware and antibody detection. For this analysis, amatrix of microparticles was synthesized, where the matrix consisted ofdifferent sets of microparticles. Each set of microparticles hadthousands of molecules of a distinct antibody capture reagentimmobilized on the micro particle surface and was color-coded byincorporation of varying amounts of two fluorescent dyes. The ratio ofthe two fluorescent dyes provided a distinct emission spectrum for eachset of microparticles, allowing the identification of a microparticlewithin a set following the pooling of the various sets ofmicroparticles. U.S. Pat. Nos. 6,268,222 and 6,599,331 also areincorporated herein by reference in their entirety, and in particularfor their teachings of various methods of labeling microparticles formultiplex analysis.

The sets of labeled beads were pooled and combined with a plasma samplefrom individuals. The labeled beads were identified by passing themsingle file through a flow device that interrogated each microparticlewith a laser beam that excited the fluorophore labels. An opticaldetector then measured the emission spectrum of each bead to classifythe beads into the appropriate set. Because the identity of eachantibody capture reagent was known for each set of microparticles, eachantibody specificity was matched with an individual microparticle thatpasses through the flow device. U.S. Pat. No. 6,592,822 is alsoincorporated herein by reference in its entirety, and in particular forits teachings of multi-analyte diagnostic system that can be used inthis type of multiplex analysis.

To determine the amount of analyte that bound a given set ofmicroparticles, a reporter molecule was added such that it formed acomplex with the antibodies bound to their respective analyte. In thepresent example, the reporter molecule was a fluorophore-labeledsecondary antibody. The fluorophore on the reporter was excited by asecond laser having a different excitation wavelength, allowing thefluorophore label on the secondary antibody to be distinguished from thefluorophores used to label the microparticles. A second optical detectormeasured the emission from the fluorophore label on the secondaryantibody to determine the amount of secondary antibody complexed withthe analyte bound by the capture antibody. In this manner, the amount ofmultiple analytes captured to beads could be measured in a singlereaction.

Data Analysis and Results.

For each sample, the concentrations of analytes that bound severaldifferent antibodies were measured. Each analyte is a biomarker, and theconcentration of each analyte in the sample can be a feature of thatbiomarker. The biomarkers were analyzed with select antibody reagentslisted in Table 14 of United States Patent Publication Number U.S.2004/0096917 A1, which is hereby incorporated herein by reference in itsentirety. These antibody reagents are commercially available from RulesBased Medicine (Austin, Tex.). The antibody reagents are categorized asspecifically binding either (1) circulating protein biomarker componentsof blood, (2) circulating antibodies that normally bind moleculesassociated with various pathogens (identified by the pathogen that eachbiomarker is associated with, where indicated), or (3) autoantibodybiomarkers that are associated with various disease states. Variousapproaches may be used to identify features that can inform a decisionrule to classify individuals into the SIRS or sepsis groups. The methodschosen were CART, MART, PAM and random forests.

Biomarkers in multiple samples were measured using the above describedassay at multiple time points and analyzed in several different ways:static time of entry, static T⁻⁶⁰, static T⁻³⁶, baseline T⁻⁶⁰, baselineT⁻³⁶, and baseline T⁻¹² data points. Representative of these analyses isthe static T⁻¹² data analysis which is described in detail below. In theT⁻¹² static analysis, biomarkers features were measured using a specificblood sample, designated the T⁻¹² blood sample, as defined in Section6.4, above.

For the T⁻¹² static analysis, there were 60 biomarkers measured on 97samples. Each sample was collected from a different member thepopulation. Of these features, 53 were transformed by logtransformations, 11 by square root transformations and the remaining 2were not transformed.

The 97 member population was initially split into a training set (n=74)and a validation set (n=23). The training set was used to estimate theappropriate classification algorithm parameters while the trainedalgorithm was applied to the validation set to independently assessperformance. Of the 74 training samples, 36 were labeled Sepsis, meaningthat the subjects developed sepsis at some point during the observationtime period, and 38 were labeled SIRS, meaning that they did not developsepsis during the observation time period. Table 82 providesdistributions of the race, gender and age for these samples.

TABLE 82 Distributions of the race, gender, and age for the trainingdata Group Gender Black Caucasian Other Sepsis Male 10 14 1 Female 0 101 SIRS Male 5 24 0 Female 0 9 0 Group Minimum Mean Median Maximum Sepsis18 43.2 40 80 SIRS 18 44.9 40 90

For the 23 validation samples, 12 were labeled Sepsis and 11 werelabeled SIRS. Table 83 provides distributions of the race, gender andage for these samples.

TABLE 83 Distributions of the race, gender, and age for the validationdata Group Gender Black Caucasian Other Sepsis Male 0 7 0 Female 0 4 0SIRS Male 2 6 0 Female 0 4 0 Group Minimum Mean Median Maximum Sepsis 1843.4 43 81 SIRS 19 51.9 51.5 85

Each sample in the training data was randomly assigned to one of tengroups used for cross-validation. The number of training samples inthese groups ranged from 6 to 8. The samples were assigned in way thatattempted to balance the number of sepsis and SIRS samples across folds.As described in more detail below, several different methods were usedto judge whether select biomarkers discriminate between the Sepsis andSIRS groups.

Wilcoxon and Q-Value Tests.

The first method used to identify discriminating biomarkers was aWilcoxon test (unadjusted). The abundance value for a given biomarkeracross the samples in the training data was subjected to the Wilcoxontest. The Wilcoxon test considers both group classification (sepsisversus SIRS) and abundance value in order to compute a p value for thegiven biomarker. The p value provides an indication of how well theabundance value for the given biomarker across the samples collected inthe training set discriminates between the sepsis and SIRS state. Thelower the p value, the better the discrimination. When the p value isless than a specific confidence level, such as 0.05, an inference ismade that the biomarker discriminates between the sepsis and SIRSphenotype. There were 24 significant biomarkers using this method (seeTable 84).

The second method used to identify discriminating biomarkers was theWilcoxon Test (adjusted). Due to the large number of biomarkers, 60, incombination with the relatively small number of samples, 97, there was ahigh risk of finding falsely significant biomarkers. An adjusted p-valuewas used to counter this risk. In particular, the method of Benjaminiand Hochberg, 1995, J.R. Statist. Soc. B 57, pp 289-300, which is herebyincorporated herein by reference in its entirety, was used to controlthe false discovery rate. Here, the false discovery rate is defined asthe number of biomarkers truly significant divided by the number ofbiomarkers declared significant. For example, if the adjusted p-value isless than 0.05, there is a 5% chance that the biomarker is a falsediscovery. Results using this test are reported in Table 84. There were16 significant biomarkers using this method (see Table 84). As used,herein, a biomarker is considered significant if it has a p-value ofless than 0.05 as determined by the Wilcoxon test (adjusted).

The third method used to identify discriminating biomarkers was the useof Q values. In such an approach, the biomarkers are ordered by theirq-values and if a respective biomarker has a q-value of X, thenrespective biomarker and all others more significant have a combinedfalse discovery rate of X. However, the false discovery rate for any onebiomarker may be much larger. There were 16 significant biomarkers usingthis method (see Table 84).

TABLE 84 Cumulative number of significant calls for the three methods.Note that all samples (training and validation) were used to compareSepsis and SIRS groups. Missing biomarker feature values were notincluded in the analyses. ≦1e−04 ≦0.001 ≦0.01 ≦0.025 ≦0.05 ≦0.1 ≦1p-value 0 6 14 20 24 25 60 (unadjusted) p-value 0 0 6 13 16 24 60(adjusted) q-value 0 0 13 20 25 31 60

CART.

In addition to analyzing the microarray data using Wilcoxon and Q-valuetests in order to identify biomarkers that discriminate between thesepsis and SIRS subpopulations in the training set, classification andregression tree (CART) analysis was used. CART is described in Section5.5.1, above. Specifically, the data summarized above was used topredict the disease state by iteratively partitioning the data based onthe best single-variable split of the data. In other words, at eachstage of the tree building process, the biomarker whose expressionvalues across the training population best discriminate between thesepsis and SIRS population was invoked as a decision branch.Cross-validation was carried out, with the optimal number of splitsestimated independently in each of the 10 iterations. The final tree isdepicted in FIG. 49, and uses ten biomarkers: MIP1beta, thrombopoietin,C reactive protein, IL-10, IL-16, beta-2 microglobulin, alphafetoprotein, IL-6, adiponectin, and ICAM1.

FIG. 50 shows the distribution of the ten biomarkers used in thedecision tree between the sepsis and SIRS groups in the training dataset. In FIG. 50, the top of each box denotes the 75^(th) percentile ofthe data across the training set and the bottom of each box denotes the25^(th) percentile, and the median value for each biomarker across thetraining set is drawn as a line within each box. The confusion matrixfor the training data where the predicted classifications were made fromthe cross-validated model is given in Table 85. From this confusionmatrix, the overall accuracy was estimated to be 63.5% with a 95%confidence interval of 51.5% to 74.4%. The estimated sensitivity was66.7% and the estimated specificity was 60.5%.

TABLE 85 Confusion matrix for training samples using cross-validatedCART True Diagnosis Predicted Sepsis SIRS Sepsis 24 15 SIRS 12 23

For the 23 validation samples held back from training data set, theoverall accuracy was estimated to be 65.2% with a 95% confidenceinterval of 42.7% to 83.6%, sensitivity 66.7% and specificity 63.6%.Table 86 shows the confusion matrix for the validation samples.

TABLE 86 Confusion matrix for validation samples using cross-validatedCART True Diagnosis Predicted Sepsis SIRS Sepsis 8 1 SIRS 4 10

Random Forests.

Another decision rule that can be developed using biomarkers of thepresent invention is a Random Forests decision tree. Random Forests is atree based method that uses bootstrapping instead of cross-validation.For each iteration, a random sample (with replacement) is drawn and thelargest tree possible is grown. Each tree receives a vote in the finalclass prediction. To fit a random forest, the number of trees (e.g.bootstrap iterations) is specified. No more than 500 were used in thisexample, but at least 50 are needed for a burn-in period. The number oftrees was chosen based on the accuracy of the training data. For thisdata, 64 trees were used to train the algorithm (see FIG. 51). In FIG.51, curve 4802 is a smoothed estimate of overall accuracy as a functionof tree number. Curve 4804 is a smoothed curve of tree sensitivity as afunction of tree number. Curve 4806 is a smoothed curve of treespecificity as a function of tree number. Using this algorithm, 34biomarkers had non-zero importance and were used in the model. Therandom forest algorithm gauges biomarker importance by the averagereduction in the training accuracy. The biomarkers were ranked by thismethod and are shown in FIG. 52. The random forest method uses a numberof different decision trees. A biomarker is considered to havediscriminating significance if it served as a decision branch of adecision tree from a significant random forest analysis. As used herein,a significant random forest analysis is one where the lower 95%confidence interval on accuracy by cross validation on a training dataset is greater than 50% and the point estimate for accuracy on avalidation set is greater than 65%.

The predicted confusion matrix for the training dataset using thedecision tree developed using the Random Forest method is given in Table87. From this confusion matrix, the overall accuracy was estimated to be70.3% (confidence intervals cannot be computed when using the bootstrapaccuracy estimate). The estimated sensitivity was 69.4% and theestimated specificity was 71.1%.

TABLE 87 Confusion matrix for training samples against the decision treedeveloped using the Random Forest method. True Diagnosis PredictedSepsis SIRS Sepsis 27 11 SIRS 11 25

For the 23 validation samples held back from training, the overallaccuracy was estimated to be 60.9% with a 95% confidence interval of38.5% to 80.3%, sensitivity 83.3% and specificity 36.4%. Table 88 showsthe confusion matrix for the validation samples.

TABLE 88 Confusion matrix for the 23 validation samples against thedecision tree developed using the Random Forest method. True DiagnosisPredicted Sepsis SIRS Sepsis 10 7 SIRS 2 4

MART.

MART was used to simultaneously assess the importance of biomarkers andclassify the subject samples. Several fitting parameters are specifiedin this approach including (i) number of trees, (ii) step size (commonlyreferred to as “shrinkage”), and (iii) degree of interaction (related tothe number of splits for each tree). More information on MART isdescribed in Section 5.5.4 above. The degree of interaction was set to 1to enforce an additive model (e.g. each tree has one split and only usesone biomarker).

The degree of interaction was set to 1 to enforce an additive model(e.g. each tree has one split and only uses one feature), because thisoften works well even when a weak interaction is present. Moreover,estimating interactions may require more data to function well. The stepsize was set to 0.05 so that the model complexity was dictated by thenumber of trees. The optimal number of trees was estimated by leavingout a random subset of cases at each fitting iteration, then assessingquality of prediction on that subset. After fitting more trees than werewarranted, the point at which prediction performance stopped improvingwas estimated as the optimal point.

The estimated model used 11 trees and 4 biomarkers across all trees. TheMART algorithm also provides a calculation of biomarker importance(summing to 100%), which are given in FIG. 53. Biomarkers with zeroimportance were excluded. FIG. 54 shows the distribution of the selectedbiomarkers between the Sepsis and SIRS groups.

Cross-validation was carried out, with the optimal number of treesestimated independently in each of the 10 iterations. The confusionmatrix for the training data where the predicted classifications weremade from the cross-validated model is given in Table 89. From thisconfusion matrix, the overall accuracy was estimated to be 70.3% with a95% confidence interval of 58.5% to 80.3%. The estimated sensitivity was63.9% and the estimated specificity was 76.3%.

TABLE 89 Confusion matrix for the training samples using thecross-validated MART algorithm. True Diagnosis Predicted Sepsis SIRSSepsis 23 9 SIRS 13 29

For the 23 validation samples held back from training, the overallaccuracy was estimated to be 73.9% with a 95% confidence interval of51.6% to 89.8%, sensitivity 63.6% and specificity 83.3%. Table 90 showsthe confusion matrix for the validation samples.

TABLE 90 Confusion matrix for the validation samples using the MARTalgorithm. True Diagnosis Predicted Sepsis SIRS Sepsis 7 2 SIRS 4 10

PAM.

Yet another decision rule developed using biomarkers of the presentinvention is predictive analysis of microarrays (PAM), which isdescribed in Section 5.5.2, above. In this method, a shrinkage parameterthat determines the number of biomarkers used to classify samples isspecified. This parameter was chosen via cross-validation. There were nobiomarkers with missing values. Based on cross-validation, the optimalthreshold value was 0.08, corresponding to 59 biomarkers. FIG. 55 showsthe accuracy across different thresholds. In FIG. 55, curve 5202 is theoverall accuracy (with 95% confidence interval bars). Curve 5204 showsdecision rule sensitivity as a function of threshold value. Curve 5206shows decision rule specificity as a function of threshold value. Usingthe threshold of 0.08, the overall accuracy for the training samples wasestimated to be 74.9 with a 95% confidence interval of 65.3% to 82.5%.The estimated sensitivity was 78.9% and the estimated specificity was69.4%. Table 91 shows the confusion matrix for the training data wherethe predicted classifications were made from the cross-validated models.

TABLE 91 Confusion matrix for training samples using cross-validated PAMalgorithm True Diagnosis Predicted Sepsis SIRS Sepsis 30 11 SIRS 8 25

For the 23 validation samples held back from training, the overallaccuracy was estimated to be 65.2% with a 95% confidence interval of42.7% to 83.6%, sensitivity 91.7% and specificity 36.4%. Table 92 showsthe confusion matrix for the validation samples.

TABLE 92 Confusion matrix for validation samples using cross-validatedPAM algorithm True Diagnosis Predicted Sepsis SIRS Sepsis 11 7 SIRS 1 4FIG. 56 shows the selected biomarkers, ranked by their relativediscriminatory power, and their relative importance in the model.

FIG. 57 provides a summary of the CART, MART, PAM, and random forests(RF) classification algorithm (decision rule) performance and associated95% confidence intervals. Fifty distinct biomarkers were selected fromacross all the algorithms illustrated in FIG. 58. The identity of thesefifty selected features is found in FIG. 58. FIG. 58 illustrates anoverall ranking of biomarkers for the T⁻¹² data set. For the selectedbiomarkers, the x-axis depicts the percentage of times that it wasselected. Within the percentage of times that biomarkers were selected,the biomarkers are ranked.

From the analysis of the T⁻¹² data set and the other data sets, tenprotein based biomarkers were selected for confirmation using themethodology described in Section 16.3.2. These biomarkers are listed inTable 93, below.

TABLE 93 Protein based biomarkers selected for confirmation fromimmunoassay Gene Protein Gene Accession Accession Symbol Gene NameNumber Number IL-6 INTERLEUKIN 6 NM_000600 NP_000591 IL-8 INTERLEUKIN 8M28130 AAA59158 CRP C Reactive protein CAA39671 NM_000567 IL-10INTERLEUKIN 10 NM_000572 CAH73907 APOC3 APOLIPOPROTEIN CIII NM_000040CAA25648 MMP9 MATRIX NM_004994 NP_004985 METALLOPROTEINASE 9 (GELATINASEB, 92 KDA GELATINASE, 92 KDA TYPE IV COLLAGENASE) TIMP1 TISSUE INHIBITOROF NM_003254 AAA75558 METALLOPROTEINASE 1 MCP1 MONOCYTE AF493698,AAQ75526 CHEMOATTRACTANT AF493697 PROTEIN 1 AFP ALPHA-FETOPROTEINNM_001134 CAA79592 B2M BETA-2 MICROGLOBULIN NM_004048 AAA51811

Each of the sequences, genes, proteins, and probesets identified inTable 93 is hereby incorporated by reference.

6.13.2 Confirmation of Protein Biomarkers Using a Bead Based ProteinImmunoassay

Confirmation of the biomarkers identified in Table 93 was performedusing the same assay described in Section 6.13.1 at multiple time pointsand analyzed in several different ways: static time of entry, staticT⁻⁶⁰, static T⁻³⁶, baseline T⁻⁶⁰, baseline T⁻³⁶, and baseline T⁻¹² datapoints. Representative of these analyses is the static T⁻¹² dataanalysis which is described in detail below. In the T⁻¹² staticanalysis, biomarkers features were measured using a specific bloodsample, designated the T⁻¹² blood sample, as defined in Section 6.4,above. FIG. 59 illustrates the results of the analysis of static T⁻¹²bead based protein assay, using CART, MART, PAM and random forests,where the static T⁻¹² time point is as described in Section 6.4. Thebest decision tree in both the training and validation datasets for CARTused six biomarkers. For both the training data and the validation data,the estimated model for MART used 4 biomarkers across all trees. A totalof 7 biomarkers were of significance in both the training and thevalidation sets using PAM. Using random forest, 4 biomarkers under studywere actually found to have discriminating significance in both thetraining and validation data sets. Based on the results of the analysisof the bead based protein immunoassay summarized in FIG. 59, each of theten protein based biomarkers identified in Section 6.13.1 were confirmedby this experiment.

6.13.3 Confirmation of Protein Biomarkers Using BD Cytometric Bead ArrayAssay

IL-6, IL-8, and IL-10 proteins were confirmed using the BD™ CytometricBead Array (CBA) assay as embodied in the BD™ CBA Human InflammationKit. Flow cytometry is an analysis tool that allows for thediscrimination of different particles on the basis of size and color.Multiplexing is the simultaneous assay of many analytes in a singlesample. CBA employs a series of particles with discrete fluorescenceintensities to simultaneously detect multiple soluble analytes. CBA iscombined with flow cytometry to create a multiplexed assay. The BD CBAsystem uses the sensitivity of amplified fluorescence detection by flowcytometry to measure soluble analytes in a particle-based immunoassay.Each bead in a CBA provides a capture surface for a specific protein andis analogous to an individually coated well in an ELISA plate. The BDCBA capture bead mixture is in suspension to allow for the detection ofmultiple analytes in a small volume sample.

The combined advantages of the broad dynamic range of fluorescentdetection via flow cytometry and the efficient capturing of analytes viasuspended particles enable CBA to use fewer sample dilutions and toobtain the value of an unknown in substantially less time (compared toconventional ELISA). The BD™ CBA Human Inflammation Kit can be used toquantitatively measure Interleukin-8 (IL-8), Interleukin-1β (IL-1β),Interleukin-6 (IL-6), Interleukin-10 (IL-10), Tumor Necrosis Factor(TNF), and Interleukin-12p70 (IL-12p70) protein levels in a singlesample. The kit performance has been optimized for analysis of specificproteins in tissue culture supernatants, EDTA plasma, and serum samples.

Six bead populations with distinct fluorescence intensities have beencoated with capture antibodies specific for IL-8, IL-1β, IL-6, IL-10,TNF, and IL-12p70 proteins. The six bead populations are mixed togetherto form the BD™ CBA which is resolved in the FL3 channel of a flowcytometer such as the BD FACScan™ or BD FACSCalibur™ flow cytometer. Thecapture beads, PE-conjugated detection antibodies, and recombinantstandards or test samples are incubated together to form sandwichcomplexes. Following acquisition of sample data using the flowcytometer, the sample results are generated in graphical and tabularformat using the BD™ CBA Analysis Software. More details about the BD™CBA Human Inflammation Kit are described in the BD™ CBA HumanInflammation Kit Instruction Manual, catalog number 551811, availablefrom BD biosciences, which is hereby incorporated by reference herein inits entirety. Using the BD™ CBA Human Inflammation Kit, the biomarkersIL-6, IL-8, and IL-10 were confirmed as discriminating between sepsisand SIRS.

6.14 Assessing Subcombinations of the Biomarkers Identified in Table I

One embodiment of the present invention encompasses any 2 or more of the53 biomarkers listed in Table I as predictors for classifying a subjectas sepsis or SIRS. One embodiment of the present invention encompassesany 3 or more of the 53 biomarkers listed in Table I as predictors forclassifying a subject as sepsis or SIRS. As such, the present inventionfurther encompasses any subcombination of the 53 biomarkers listed inTable I as predictors for classifying a subject as sepsis or SIRSprovided that there are at least 2 or 3 biomarkers in thesubcombination. This section discloses experiments that demonstrate thepredictive power of exemplary subcombinations of the 53 biomarkerslisted in Table I. Several thousand subcombinations were tested and thevast majority of those subcombinations had an accuracy of at leastseventy percent. This indicates that the vast majority of the possiblesubcombinations of the 53 biomarkers listed in Table I will discriminatebetween sepsis and SIRS subjects.

6.14.1 Subcombinations of Nucleic Acid Biomarkers at T⁻¹²

There are a total of 44 biomarkers for which RT-PCR nucleic acid data isavailable as reported in Table J. A total of 4800 differentsubcombinations of this set of biomarkers were constructed using theT⁻¹² time point data described in Section 6.12. Each differentsubcombination was then tested for its ability to discriminate betweensepsis subjects and SIRS subjects. The 4800 subcombinations represent arandom sampling of the total number of possible subcombinations possiblefor the 44 biomarkers of the present invention reported in Table J.Randomness of the 4800 subcombinations was ensured using the followingalgorithm:

CONSIDER 2 to 25 biomarkers from Table J {  LET the current number be k; DO the following 200 times  {   SELECT k biomarkers at random fromTable J;   LET the current set of biomarkers be S;  }  DO the following10 times  {   FOR biomarker set S, randomly set aside 10% of patients asa   validation population and 90% as a training population;   FIT amodel to the training population using Random Forest with T⁻¹²   timepoint data;   PREDICT results for the validation population;   CALCULATEagreement with the known status of the validation   population;  } AVERAGE the ten agreement rates and report;  SET k = k+1;  IF k > 10then END; ELSE return to top; } END

There were a total of 152 patients for which T⁻¹² data was availablefrom a combination of discovery and confirmatory data described above.Of these 152 patients, 80 were sepsis and 72 were SIRs. The calculationsdescribed above test 200 subcombinations at each interval 2 through 25.In other words, 200 subcombinations each consisting of two biomarkersrandomly selected from Table J were tested, 200 subcombinations eachconsisting of three biomarkers randomly selected from Table J weretested, and so forth, through 200 subcombinations each consisting oftwenty-five biomarkers randomly selected from Table J for a total of 24families of subcombinations, where each family of subcombinationsconsists of 200 subcombinations of biomarkers each having k biomarkers,where k is a number in the set 2 through 25.

The data set with assay results for all biomarkers under considerationwas maintained in memory, as were a list of unique biomarker names. Toevaluate subsets of a specific size (say, k=three), then that many(three) biomarker names were selected randomly from the set of uniquebiomarker names, using a pseudorandom number generators provided in theR software package. See Venables and Smith, An Introduction to R, ISBN0-9541617-4-2, which is hereby incorporated by reference in itsentirety. A matrix of assay results for the selected biomarker names wasconstructed. This matrix could have more columns than the number ofselected biomarker names, since some biomarkers have more than one assayresult. An estimate of true predictive accuracy, when using the modelingtechnique “Random Forest,” was then constructed for this matrix. TheRandom Forest algorithm was implemented as described in Breiman, 2001,“Random Forests,” Machine Learning 45(1), pp. 5-32, which is herebyincorporated by reference in its entirety.

For a given data matrix, the prediction of true predictive accuracy wascalculated as follows: 10% of patients were randomly selected in abalanced manner, i.e., 10% of septic patients and 10% of SIRS patientswere selected. Selected patients were set aside for the validationpopulation, and a random forest model was fitted to the remaining data(the training population). If there were any missing values in thetraining data or the set-aside data, a recursive partitioning model wasfitted to other assay results as well as the Sepsis/SIRS information inorder to predict assay values. The recursive partitioning model isdescribed in Breiman et al., 1984, Classification and Regression Trees,Wadsworth, which is hereby incorporated by reference in its entirety.Missing values were then replaced with their predicted values. Missingvalues in the set-aside data were replaced with predictions from therecursive-partition model fitted to the training data, so that knowledgeof the SIRS/Sepsis status for the validation population was not used inany way to classify validation patients.

By comparing the true status of the 10% set aside (the validationpopulation) with the predicted status according to the random forestmodel fitted to the other 90% (the training population), sensitivity,specificity, and agreement were calculated. This process was repeated 10times, and the final sensitivity, specificity, and agreement estimates(also termed accuracy, also termed performance) for the given markersubset were those values averaged across the 10 iterations. This processwas applied to every subset. For each size considered (k value, i.e.,number of biomarkers), 200 random subsets were selected and evaluated.These 200 performance (accuracy) estimates form an estimate of thedistribution of performances of all subsets of biomarkers of a givensize (k value).

FIG. 60 plots the accuracy of each of these 24 families ofsubcombinations as bar graphs. FIG. 61 plots the accuracy (performance)of each individual subcombination in each of the 24 families ofsubcombinations. Thus, FIG. 61 plots the accuracy (performance) of atotal of 4800 subcombinations of the set of biomarkers listed in TableJ.

FIGS. 60 and 61 indicate that for k>5, the distributions are Gaussian,(bell-shaped), indicating that each respective family (k=5, . . . , 24)is an accurate depiction of the subcombination space represented by thefamily. For k<=5, a handful of subsets give lower accuracy (performance)estimates. However, the results available for k<=5 indicate that thisclass of biomarker subcombinations discriminate between sepsis and SIRSas well. The results reported in FIGS. 60 and 61 show that, with as fewas two biomarkers randomly selected from Table J, an accuracy(performance) estimate above 50% was virtually always obtained. Table 94contains the number of subcombinations in each family (k=2, 4, . . . ,25) that performed with a threshold accuracy of greater than 60% (column2), greater than 70% (column 3), greater than 80% (column 4), greaterthan 90% (column 5), or an accuracy of less than 60% (column 6). Thedata summarized in FIGS. 60 and 61, as well as Table 94, demonstratesthat, for time T⁻¹² data, almost all subcombinations of biomarkerscomprising between 2 and 25 biomarkers from Table J will discriminatebetween sepsis and SIRS subjects.

TABLE 94 Number of subcombinations from Table J that performed with agiven threshold accuracy using T⁻¹² nucleic acid data Column 1 Column 2Column 3 Column 4 Column 5 Column 6 Number of Greater Greater GreaterGreater Less than Biomarkers than 60% than 70% than 80% than 90% 60% 2197 145 17 0 3 3 199 160 25 0 1 4 199 174 38 0 1 5 200 186 51 0 0 6 200194 50 0 0 7 200 195 48 0 0 8 200 194 64 0 0 9 200 200 61 1 0 10 200 19664 0 0 11 200 196 70 0 0 12 200 199 70 0 0 13 200 199 73 0 0 14 200 19867 0 0 15 200 198 79 0 0 16 200 198 70 0 0 17 200 198 64 0 0 18 200 19984 0 0 19 200 197 83 0 0 20 200 199 82 0 0 21 200 199 85 0 0 22 200 19880 0 0 23 200 200 83 0 0 24 200 198 83 0 0 25 200 198 81 0 0

6.14.2 Subcombinations of Protein Biomarkers at T⁻¹²

There are a total of 10 biomarkers for which protein abundance data isavailable as reported in Table K. A total of 1600 differentsubcombinations of this set of biomarkers were constructed using theT⁻¹² time point data described in Section 6.13. Each differentsubcombination was then tested for its ability to discriminate betweensepsis and SIRS subjects. The 1600 subcombinations represent a randomsampling of the total number of possible subcombinations possible forthe 10 biomarkers of the present invention reported in Table K.Randomness of the 1600 subcombinations was ensured using the followingalgorithm:

CONSIDER 3 to 10 biomarkers from Table K {  LET the current number be k; DO the following 200 times  {   SELECT k biomarkers at random fromTable K;   LET the current set of biomarkers be S;  }  DO the following10 times  {   FOR biomarker set S, randomly set aside 10% of patients asa   validation population and 90% as a training population;   FIT amodel to the training population using Random Forest with T⁻¹²   timepoint data;   PREDICT results for the validation population;   CALCULATEagreement with the known status of the validation   population;  } AVERAGE the ten agreement rates and report;  SET k = k+1;  IF k > 10then END; ELSE return to top; } END

Computations were performed as described in further detail in Section6.14.1. There were a total of 152 patients for which T⁻¹² data wasavailable from a combination of discovery and confirmatory datadescribed above. Of these 152 patients, 80 were sepsis and 72 were SIRs.For some biomarkers in Table K, there were multiple data sources. Forinstance, there is IL-6, IL-8, and IL-10 protein data from threedifferent labs. Thus, there is a complex pattern of incidence amongpatients. Some patients may be tested by one lab, others by two, etc.This was determined by how the project evolved and what samples wereavailable (some patient samples were exhausted before they could betested with assays developed later). To handle this complex incidence,the following strategy was used. In any given iteration, if a proteinthat was tested in multiple labs was selected, all assay results for theprotein were selected. A missing-value imputation scheme was then usedto fill out missing values, making it look like all patients were testedwith all assays. This data was then fed into the Random Forest model ascorrelated inputs that measure the same underlying compound. Thus,consider the case where k is equal to 3 and one of the randomly chosenproteins from Table K is IL-8, from 3 different laboratories, and theother 2 proteins are unique meaning that they are each from only onelaboratory. The data from all three IL-8 sources are selected, plus theother two unique assays for the other two proteins, for a total of fiveassays. The missing-value imputation scheme is then used to fill outmissing values, making it look like all patients had results from threedifferent sources for a total of nine assays.

The calculations described above test 200 subcombinations at eachinterval 3 through 10. In other words, 200 subcombinations eachconsisting of three randomly selected biomarkers from Table K weretested, 200 subcombinations each consisting of four biomarkers randomlyselected from Table K were tested, and so forth, through 200subcombinations each consisting of ten biomarkers randomly selected fromTable K for a total of 8 families of subcombinations, where each familyof subcombinations consists of 200 subcombinations of biomarkers allhaving k biomarkers, where k is a number in the set 3 through 10. FIG.62 plots the accuracy of each of these eight families of subcombinationsas bar graphs. FIG. 63 plots the accuracy (performance) of eachindividual subcombination in each of the eight families ofsubcombinations. Thus, FIG. 63 plots the accuracy (performance) of atotal of 1600 subcombinations of the set of biomarkers listed in TableK.

FIGS. 62 and 63 show that the distribution for each family ofsubcombinations is Gaussian (bell-shaped), indicating that eachrespective family (k=3, 4, . . . , 10) is an accurate depiction of thesubcombination space represented by the family. The results reported inFIGS. 62 and 63 show that, with as few as three biomarkers randomlyselected from Table K, an accuracy (performance) estimate above 50% wasvirtually always obtained. Table 95 contains the number ofsubcombinations in each family (k=3, 4, . . . , 10) that performed witha threshold accuracy of greater than 60% (column 2), greater than 70%(column 3), greater than 80% (column 4), greater than 90% (column 5), oran accuracy of less than 60% (column 6). The data summarized in FIGS. 62and 63, as well as Table 95, demonstrates that, for time T⁻¹² data,almost all subcombinations of biomarkers comprising between 3 and 10biomarkers from Table K will discriminate between sepsis and SIRSsubjects.

TABLE 95 Number of subcombinations from Table K that performed with agiven threshold accuracy using T⁻¹² protein data Column 1 Column 2Column 3 Column 4 Column 5 Column 6 Number of Greater Greater GreaterGreater Less than Biomarkers than 60% than 70% than 80% than 90% 60% 3192 58 0 0 8 4 196 106 1 0 4 5 200 117 1 0 0 6 200 136 0 0 0 7 200 151 10 0 8 200 172 1 0 0 9 200 188 1 0 0 10 200 187 0 0 0

6.14.3 Subcombinations of Protein Biomarkers at T⁻³⁶

A total of 1600 different subcombinations of the set of biomarkers ofTable K were constructed for the T⁻³⁶ time point using the protein baseddata described in Section 6.13. Each different subcombination was thentested for its ability to discriminate between sepsis and SIRS subjects.There were a total of 142 patients for which T⁻³⁶ data was availablefrom a combination of discovery and confirmatory data described above.Of these 142 patients, 79 were sepsis and 63 were SIRs. The 1600subcombinations represent a random sampling of the total number ofpossible subcombinations possible for the 10 biomarkers of the presentinvention reported in Table K. Randomness of the 1600 subcombinationswas ensured using the algorithm identified in Section 6.14.2, the onlydifference being that T⁻³⁶ data rather than T⁻¹² data was used.Computations were performed as described in further detail in Section6.14.1. FIG. 64 plots the accuracy of each of these eight families ofsubcombinations as bar graphs. FIG. 65 plots the accuracy (performance)of each individual subcombination in each of the eight families ofsubcombinations. Thus, FIG. 65 plots the accuracy (performance) of atotal of 1600 subcombinations of the set of biomarkers listed in TableK.

The results reported in FIGS. 64 and 65 show that, with as few as threebiomarkers randomly selected from Table K, an accuracy (performance)estimate above 60% was typically obtained. Table 95 contains the numberof subcombinations in each family (k=3, 4, . . . , 10) that performedwith a threshold accuracy of greater than 60% (column 2), greater than70% (column 3), greater than 80% (column 4), greater than 90% (column5), or an accuracy of less than 60% (column 6). The data summarized inFIGS. 64 and 65, as well as Table 95, demonstrates that, for time T⁻³⁶data, most subcombinations of biomarkers comprising between 3 and 10biomarkers from Table K will discriminate between sepsis and SIRssubjects.

TABLE 96 Number of subcombinations from Table K that performed with agiven threshold accuracy using T⁻³⁶ protein data Column 1 Column 2Column 3 Column 4 Column 5 Column 6 Number of Greater Greater GreaterGreater Less than Biomarkers than 60% than 70% than 80% than 90% 60% 3138 23 0 0 62 4 152 25 0 0 48 5 171 32 0 0 29 6 180 30 0 0 20 7 188 37 00 12 8 194 40 0 0 6 9 194 27 0 0 6 10 199 20 0 0 1

6.14.4 Subcombinations of Nucleic Acid Biomarkers at T⁻³⁶

A total of 4600 different subcombinations of the set of biomarkers ofTable J were constructed for the T⁻³⁶ time point using the nucleic acidbased data described in Section 6.13. Each different subcombination wasthen tested for its ability to discriminate between sepsis and SIRSsubjects. There were a total of 142 patients for which T⁻³⁶ data wasavailable from a combination of discovery and confirmatory datadescribed above. Of these 142 patients, 79 were sepsis and 63 were SIRs.The 4600 subcombinations represent a random sampling of the total numberof possible subcombinations possible for the 44 biomarkers of thepresent invention reported in Table J at the T⁻³⁶ time point. Randomnessof the 4600 subcombinations was ensured using the algorithm identifiedin Section 6.14.1, the only difference being that T⁻³⁶ data rather thanT⁻¹² data was used and that the minimum k value was 3. Computations wereperformed as described in further detail in Section 6.14.1. FIG. 66plots the accuracy of each of these 23 families of subcombinations asbar graphs. FIG. 67 plots the accuracy (performance) of each individualsubcombination in each of the 23 families of subcombinations. Thus, FIG.67 plots the accuracy (performance) of a total of 4600 subcombinationsof the set of biomarkers listed in Table J.

The results reported in FIGS. 66 and 67 show that, with as few as threebiomarkers randomly selected from Table J, an accuracy (performance)estimate above 60% was typically obtained. Table 97 contains the numberof subcombinations in each family (k=3, 4, . . . , 25) that performedwith a threshold accuracy of greater than 60% (column 2), greater than70% (column 3), greater than 80% (column 4), greater than 90% (column5), or an accuracy of less than 60% (column 6). The data summarized inFIGS. 66 and 67, as well as Table 97, demonstrate that, for time T⁻³⁶data, most subcombinations of biomarkers comprising between 3 and 25biomarkers from Table J will discriminate between sepsis and SIRssubjects.

TABLE 97 Number of subcombinations from Table J that performed with agiven threshold accuracy using T⁻³⁶ nucleic acid data Column 1 Column 2Column 3 Column 4 Column 5 Column 6 Number of Greater Greater GreaterGreater Less than Biomarkers than 60% than 70% than 80% than 90% 60% 3194 115 4 0 6 4 196 128 8 0 4 5 200 159 6 0 0 6 200 161 6 0 0 7 200 17310 0 0 8 200 182 10 0 0 9 200 189 8 0 0 10 200 188 10 0 0 11 200 194 100 0 12 200 193 13 0 0 13 200 190 19 0 0 14 200 192 24 0 0 15 200 196 200 0 16 200 195 16 0 0 17 200 196 22 0 0 18 200 195 18 0 0 19 200 196 140 0 20 200 194 14 0 0 21 200 197 14 0 0 22 200 197 11 0 0 23 200 200 150 0 24 200 199 20 0 0 25 200 199 19 0 0

6.14.5 Subcombinations of Combined Nucleic Acid and Protein BiomarkerData at T⁻¹²

There are a total of 53 biomarkers listed Table I. A total of 4600different subcombinations of this set of biomarkers were constructedusing all available T⁻¹² time point data. For the subset of biomarkersin Table I that are listed in Table J, the T⁻¹² time point dataconsisted of RT-PCR data described above. For the subset of biomarkersthat are listed in Table K, the T⁻¹² time point data consisted of beadbased data described above. The one exception to this was “MMP9” forwhich both protein and gene-expression data was available. Therefore,MMP9 gene and protein abundance data was treated as separate biomarkers.To accomplish this, MMP9 nucleic data was termed “MMP9.GE” and MMP9protein abundance data from the bead based assays was termedMMP9.Protein.

Each different subcombination was tested for its ability to discriminatebetween sepsis subjects and SIRS subjects. The 4600 subcombinationsrepresent a random sampling of the total number of possiblesubcombinations possible for the 53 biomarkers of the present inventionreported in Table I. Randomness of the 4600 subcombinations was ensuredusing the following algorithm:

CONSIDER 3 to 25 biomarkers from Table I {  LET the current number be k; DO the following 200 times  {   SELECT k biomarkers at random fromTable I;   LET the current set of biomarkers be S;  }  DO the following10 times  {   FOR biomarker set S, randomly set aside 10% of patients asa   validation population and 90% as a training population;   FIT amodel to the training population using Random Forest with T⁻¹²   timepoint data;   PREDICT results for the validation population;   CALCULATEagreement with the known status of the validation   population;  } AVERAGE the ten agreement rates and report;  SET k = k+1;  IF k > 10then END; ELSE return to top; } END

There were a total of 152 patients for which T⁻¹² data was availablefrom a combination of discovery and confirmatory data described above.Of these 152 patients, 80 were sepsis and 72 were SIRs. Computationswere performed as described in further detail in Section 6.14.1. Thecalculations described above test 200 subcombinations at each interval 2through 25. In other words, 200 subcombinations each consisting of threebiomarkers randomly selected from Table I were tested, 200subcombinations each consisting of four biomarkers randomly selectedfrom Table I were tested, and so forth, through 200 subcombinations eachconsisting of twenty-five biomarkers randomly selected from Table I fora total of 23 families of subcombinations, where each family ofsubcombinations consists of 200 subcombinations of biomarkers eachhaving k biomarkers, where k is a number in the set 3 through 25. FIG.68 plots the accuracy of each of these 23 families of subcombinations asbar graphs. FIG. 69 plots the accuracy (performance) of each individualsubcombination in each of the 23 families of subcombinations. Thus, FIG.69 plots the accuracy (performance) of a total of 4600 subcombinationsof the set of biomarkers listed in Table I.

FIGS. 68 and 69 indicate that for k>5, the distributions are Gaussian,(bell-shaped), indicating that each respective family (k=5, . . . , 25)is an accurate depiction of the subcombination space represented by thefamily. For k<=5, a handful of subsets give lower accuracy (performance)estimates. The results reported in FIGS. 68 and 69 show that, with asfew as three biomarkers randomly selected from Table I, an accuracy(performance) estimate above 50% was virtually always obtained. Table 98contains the number of subcombinations in each family (k=3, 4, . . . ,25) that performed with a threshold accuracy of greater than 60% (column2), greater than 70% (column 3), greater than 80% (column 4), greaterthan 90% (column 5), or an accuracy of less than 60% (column 6). Thedata summarized in FIGS. 68 and 69, as well as Table 98, demonstratethat, for time T⁻¹² data, almost all subcombinations of biomarkerscomprising between 3 and 25 biomarkers from Table I will discriminatebetween sepsis and SIRs subjects.

TABLE 98 Number of subcombinations from Table I that performed with agiven threshold accuracy using T⁻¹² combined nucleic acid and proteindata Column 1 Column 2 Column 3 Column 4 Column 5 Column 6 Number ofGreater Greater Greater Greater Less than Biomarkers than 60% than 70%than 80% than 90% 60% 3 200 157 11 0 0 4 200 166 12 0 0 5 200 191 25 0 06 200 191 30 0 0 7 200 195 33 0 0 8 200 198 44 0 0 9 200 200 49 0 0 10200 198 63 0 0 11 200 199 65 0 0 12 200 200 71 0 0 13 200 200 64 0 0 14200 200 66 0 0 15 200 200 74 0 0 16 200 199 74 0 0 17 200 200 73 0 0 18200 200 76 0 0 19 200 200 87 0 0 20 200 199 94 0 0 21 200 200 77 0 0 22200 200 84 0 0 23 200 200 81 0 0 24 200 200 93 0 0 25 200 200 85 0 0

6.14.6 Subcombinations of Combined Nucleic Acid and Protein BiomarkerData at T⁻³⁶

Subcombinations of biomarkers were selected as described in Section6.14.5, the only difference being that T⁻³⁶ data rather than T⁻¹² datawas used. A total of 4600 different subcombinations of Table I wereconstructed using all available T⁻³⁶ time point data. There were a totalof 142 patients for which T⁻³⁶ data was available from a combination ofdiscovery and confirmatory data described above. Of these 142 patients,79 were sepsis and 63 were SIRs. Computations were performed asdescribed in further detail in Section 6.14.1. The calculationsdescribed above test 200 subcombinations at each interval 3 through 25.In other words, 200 subcombinations each consisting of three biomarkersrandomly selected from Table I were tested, 200 subcombinations eachconsisting of four biomarkers randomly selected from Table I weretested, and so forth, through 200 subcombinations each consisting oftwenty-five biomarkers randomly selected from Table I for a total of 23families of subcombinations, where each family of subcombinationsconsists of 200 subcombinations of biomarkers each having k biomarkers,where k is a number in the set 3 through 25. FIG. 70 plots the accuracyof each of these 23 families of subcombinations as bar graphs. FIG. 71plots the accuracy (performance) of each individual subcombination ineach of the 23 families of subcombinations. Thus, FIG. 71 plots theaccuracy (performance) of a total of 4600 subcombinations of the set ofbiomarkers listed in Table I.

FIGS. 70 and 71 indicate that for k>5, the distributions are Gaussian,(bell-shaped), indicating that each respective family (k=5, . . . , 25)is an accurate depiction of the subcombination space represented by thefamily. For k<=5, a handful of subsets give lower accuracy (performance)estimates. The results reported in FIGS. 70 and 71 show that, with asfew as three biomarkers randomly selected from Table I, an accuracy(performance) estimate above 50% was virtually always obtained. Table 99contains the number of subcombinations in each family (k=3, 4, . . . ,25) that performed with a threshold accuracy of greater than 60% (column2), greater than 70% (column 3), greater than 80% (column 4), greaterthan 90% (column 5), or an accuracy of less than 60% (column 6). Thedata summarized in FIGS. 70 and 71, as well as Table 99, demonstratethat, for time T⁻³⁶ data, almost all subcombinations of biomarkerscomprising between 3 and 25 biomarkers from Table I will discriminatebetween sepsis and SIRs subjects.

TABLE 99 Number of subcombinations from Table I that performed with agiven threshold accuracy using T⁻³⁶ combined nucleic acid and proteindata Column 1 Column 2 Column 3 Column 4 Column 5 Column 6 Number ofGreater Greater Greater Greater Less than Biomarkers than 60% than 70%than 80% than 90% 60% 3 187 96 4 0 13 4 199 127 5 0 1 5 200 145 7 0 0 6200 149 9 0 0 7 200 148 9 0 0 8 200 157 8 0 0 9 200 175 13 0 0 10 199179 7 0 1 11 200 180 11 0 0 12 200 175 11 0 0 13 200 184 15 0 0 14 200184 10 0 0 15 200 180 5 0 0 16 200 190 17 0 0 17 200 191 16 0 0 18 200190 15 0 0 19 200 191 8 0 0 20 200 196 13 0 0 21 200 198 15 0 0 22 200195 9 0 0 23 200 195 14 0 0 24 200 195 10 0 0 25 200 196 18 0 0

6.15 Mean Expression Value of Biomarkers in Sepsis and SIRS PatientsIdentified in Table I

The mean expression values of the biomarkers of Table I were determinedfor subjects that acquired sepsis (Sepis subjects) and subjects that didnot acquire sepsis (SIRS subjects) in the populations described inSections 6.11.2, 6.12.2 (Affymetrix data), 611.1, 6.12.1 (RT-PCR data),6.13.3 (bead data), and 6.13.1 and 6.13.2 (bead data) at the T⁻¹², T⁻³⁶,and T⁻⁶⁰ time points. This data is set forth in Table 100 below. InTable 100, a biomarker with the _Affy extension represents the combineddata of Sections 6.11.2 and 6.11.2 (Affymetrix data), a biomarker withthe 0.18S extension represents the combined data of Sections 6.11.1 and6.12.1 (RT-PCR data), a biomarker with the BDB extension represents thedata of Section 6.13.3, and a biomarker with the RBM extensionrepresents the data of Sections 6.13.1 and 6.13.2.

For nucleic acid biomarkers in Table 100 that were identified by genearrays (.Affy), the values in Table 100 represent mean relativefluorescence intensity units obtained for the specific probe sequencesexamined. As such, they are not actual units of measure, just relativequantity of one group to another. Additionally, as part of the dataanalysis, some of these values may have undergone a log transformationor other adjustment, prior to being reported. The expression values forthe nucleic acid biomarkers (0.18S) found in Table 100 are defined bythe relative “cycle-time to threshold.” As such, they do not cite actualunits of measure, just relative quantity of one group to another. Asample with a higher amount of nucleic acid will become positive sooner(fewer cycles) than one with less nucleic acid, which will require morecycles before the resultant signal crosses a positivity threshold. Forthe protein biomarkers in Table 101, the units were as followsalphafetoprotein (AFP) μg/mL (micrograms per milliliter of plasma),Beta-2-Microglobulin B2M) μg/mL, Interleukin-6 (IL-6) pg/mL(picograms/milliliter), Interleukin-8 (IL-8) pg/mL, Interleukin-10(IL-10) pg/mL, Monocyte Chemoatractant Protein 1 (MCP) pg/mL, MatrixMetalloproteinase 9 (MMP9) ng/mL (nanogram/milliliter), Tissue Inhibitorof Metalloproteinase 1 (TIMP1) ng/mL (nanogram/mL), C Reactive protein(CRP) μg/mL, and Apoliprotein CIII μg/mL.

TABLE 100 Mean expression values for the biomarkers of Table I asmeasured in the experimental Affymetrix, RT-PCR, and bead data ofSection 6. SIRS Sepsis SIRS Sepsis SIRS Sepsis Marker T⁻¹² T⁻¹² T⁻³⁶T⁻³⁶ T⁻⁶⁰ T⁻⁶⁰ ANKRD22_Affy 8.33 9.72 8.28 9.27 8.51 9.17 ANXA3_Affy10.06 11.28 10.19 11.197 10.49 11.14 BCL2A1_Affy 8.55 9.87 8.73 9.489.01 9.60 CCL5_Affy 11.69 11.30 11.73 11.22 11.49 11.29 CD86_Affy 8.598.10 8.68 8.13 8.57 8.20 CEACAM1_Affy 7.81 8.74 7.83 8.43 7.91 8.43CRTAP_Affy 9.30 8.81 9.31 8.87 9.31 8.96 CSF1R_Affy 9.36 8.79 9.34 8.939.40 8.89 FAD104_Affy 7.89 8.63 8.04 8.39 8.18 8.36 FCGR1A_Affy 7.388.10 7.34 7.90 7.53 7.76 GADD45A_Affy 8.26 9.03 8.36 8.95 8.53 9.01GADD45B_Affy 8.81 9.40 8.80 9.21 8.91 9.20 HLA.DRA_Affy 11.91 11.2211.80 11.13 11.78 11.28 IFNGR1_Affy 11.20 11.51 11.25 11.53 11.38 11.52IL18Rl_Affy 6.65 8.23 6.81 7.90 7.00 7.95 INSL3_Affy 6.93 7.37 7.01 7.317.10 7.23 IRAK2_Affy 7.13 7.66 7.20 7.52 7.19 7.50 IRAK4_Affy 7.76 8.0527.90 7.90 7.88 7.96 ITGAM_Affy 11.26 11.80 11.36 11.78 11.53 11.78JAK2_Affy 6.85 7.523 7.02 7.35 7.08 7.38 LDLR_Affy 7.05 7.788 7.09 7.767.12 7.75 LY96_Affy 9.50 10.26 9.65 10.03 9.76 10.02 MAP2K6_Affy 8.2619.17 8.49 9.02 8.59 9.05 MAPK14_Affy 8.75 9.75 8.99 9.51 9.16 9.48MKNKl_Affy 10.02 10.59 10.07 10.50 10.30 10.60 Gene_MMP9_Affy 12.0513.03 12.28 12.95 12.41 12.93 NCR1_Affy 5.64 5.94 5.645 5.90 5.77 5.91OSM_Affy 6.70 7.51 6.79 7.25 7.00 7.32 PFKFB3_Affy 9.60 10.92 9.78 10.8310.18 10.82 PRV1_Affy 9.79 11.99 9.89 11.81 10.58 11.72 PSTPIP2_Affv8.63 9.64 8.68 9.45 8.90 9.44 SOCS3_Affy 7.26 8.43 7.31 8.10 7.69 8.12SOD2_Affy 9.94 10.66 10.07 10.69 10.27 10.59 TDRD9_Affy 6.84 8.30 7.028.30 7.43 8.18 TGFBI_Affy 10.17 9.31 10.27 9.45 10.37 9.63 TIFA_Affy5.99 6.51 5.94 6.31 6.01 6.27 TNFSF10_Affy 10.38 10.77 10.51 10.56 10.4710.52 TNFSF13B_Affy 10.23 10.70 10.24 10.56 10.48 10.58 IL10alpha_Affy9.91 9.53 9.87 9.56 9.91 9.63 ANKRD22.18S 20.35 18.33 20.31 18.65 20.0118.70 ANXA3.18S 17.29 15.52 17.00 15.50 16.78 15.68 ARG2.18S 20.36 19.0920.12 19.26 19.93 19.13 BCL2A1.18S 18.58 17.00 18.20 17.05 17.84 17.04CD86.18S 19.81 20.19 19.70 20.15 19.68 20.10 CEACAM1.18S 19.98 18.3419.84 18.39 19.73 18.36 FCGR1A.18S 16.78 15.03 16.51 15.32 16.329 15.59GADD45A.18S 19.73 18.46 19.69 18.45 19.38 18.53 GADD45B.18S 16.72 15.5216.61 15.65 16.50 15.86 IFNGR1.18S 16.00 15.23 15.77 15.20 15.68 15.23IL1RN.18S 17.24 16.01 17.03 16.05 16.95 16.27 IL18R1.18S 20.56 18.7120.15 18.72 20.04 18.72 INSL3.18S 21.41 20.03 21.22 20.01 21.00 20.22IRAK2.18S 20.45 19.20 20.28 19.29 20.32 19.49 IRAK4.18S 18.25 17.7418.06 17.65 17.98 17.71 ITGAM.18S 15.45 14.51 15.27 14.57 15.12 14.60JAK2.18S 17.77 16.90 17.56 16.97 17.50 17.06 LDLR.18S 20.34 19.31 20.1919.10 20.28 19.24 LY96.18S 19.24 18.20 18.95 18.27 18.75 18.35MAP2K6.18S 18.11 16.78 17.86 16.62 17.73 16.74 MKNK1.18S 17.61 16.4817.37 16.48 17.22 16.58 Gene_MMP9.18S 14.89 13.40 14.70 13.27 14.5313.15 NCR1.18S 21.89 21.06 21.79 20.99 21.82 21.11 OSM.18S 19.98 18.4119.71 18.51 19.49 18.57 PFKFB3.18S 17.83 15.86 17.53 15.85 17.IS 16.00PRV1.18S 16.87 13.49 16.28 13.08 15.42 13.34 PSTP1P2.18S 17.45 16.1817.24 16.31 17.13 16.37 SOCS3.18S 16.83 15.09 16.57 15.12 16.20 15.30SOD2.18S 13.62 12.83 13.50 12.87 13.41 13.12 TDRD9.18S 22.65 20.64 22.3220.45 21.95 20.57 TIFA.18S 19.02 17.40 18.89 17.69 18.70 17.97 TLR4.18S17.93 17.03 17.83 17.24 17.73 17.33 TNFRSF6.18S 17.31 16.51 17.02 16.7417.05 16.88 TNFSF10.18S 16.21 15.32 15.98 15.49 16.00 15.68 TNFSF13B.18S16.32 15.43 16.16 15.72 16.01 15.71 VNN1.18S 17.16 15.39 16.70 15.1916.51 15.12 AlphaFetoprotein_RBM 3.77 4.22 3.70 4.34 3.34 4.01ApolipoproteinCIII_RBM 59.72 38.05 53.84 38.34 53.38 40.03Beta2Microglobulin_RBM 2.83 3.63 2.57 3.02 2.43 2.89CReactiveProtein_RBM 146.95 261.16 206.21 258.20 171.76 245.76 IL6_RBM82.87 4858.72 99.21 308.50 98.28 341.38 IL8_RBM 31.03 121.58 36.78 78.9330.15 69.30 IL10_RBM 21.92 64.45 33.16 38.60 21.12 37.43 MCP1_RBM 237.84796.87 259.82 505.33 229.04 558.73 Protein_MMP9_RBM 1143.25 1809.30992.84 1390.05 961.12 1364.40 TIMP1_RBM 226.68 406.90 237.22 351.29235.05 344.12 ARG2_SPM 21.34 20.25 21.30 20.43 21.32 20.56 CD86_SPM17.31 17.95 17.47 17.90 17.53 17.87 FCGR1A_SPM 16.16 14.48 15.98 14.8515.80 15.13 IL1RN_SPM 15.75 14.81 15.59 14.95 15.55 15.10 IL6_SPM 23.5823.55 23.71 23.67 23.70 23.58 IL8_SPM 21.97 21.99 22.46 22.48 22.6822.41 IL10_SPM 21.15 20.02 21.10 20.26 20.96 20.24 IL18R1_SPM 18.3316.54 18.06 16.75 18.04 16.83 ITGAM_SPM 14.66 13.82 14.37 13.84 14.3713.96 Gene_MMP9_SPM 13.14 11.71 12.90 11.82 13.01 11.95 TIMP1_SPM 13.5713.00 13.38 12.95 13.47 13.02 TLR4_SPM 14.86 14.22 14.80 14.35 14.7114.41 TNFSF13B_SPM 14.63 13.91 14.56 14.13 14.52 14.21CReactiveProtein_SPM 23.19 23.00 23.26 23.20 23.27 23.25 IL6_BDB −0.150.59 −0.25 −0.02 −0.19 0.147 IL8_BDB −0.24 0.74 −0.30 0.24 −0.32 0.20IL10_BDB −0.18 0.57 −0.27 0.35 −0.20 0.10 MCP1_BDB −0.18 0.58 −0.18 0.01−0.25 0.20

The range of expression values of the biomarkers of Table I weredetermined for subjects that acquired sepsis (Sepis subjects) andsubjects that did not acquire sepsis (SIRS subjects) in the populationsdescribed in Sections 6.11.2, 6.12.2 (Affymetrix data), 611.1, 6.12.1(RT-PCR data), 6.13.3 (bead data), and 6.13.1 and 6.13.2 (bead data) atthe T⁻¹², and T⁻³⁶ time points. This data is set forth in Table 101below. In Table 101, a biomarker with the _Affy extension represents thecombined data of Sections 6.11.2 and 6.11.2 (Affymetrix data), abiomarker with the 0.18S extension represents the combined data ofSections 6.11.1 and 6.12.1 (RT-PCR data), a biomarker with the BDBextension represents the data of Section 6.13.3, and a biomarker withthe RBM extension represents the data of Sections 6.13.1 and 6.13.2.Time points are given in column 6, where T-12 represents the T⁻¹² timepoint, and T-36 represents the T⁻³⁶ time point. Units in Table 101 areas described for Table 100.

TABLE 101 Expression value ranges for the biomarkers of Table I asmeasured in the experimental Affymetrix, RT-PCR, and bead data ofSection 6. Minimum value Maximum value Minimum value Maximum valueBiomarker in sepsis subjects in sepsis subjects in SIRS subjects in SIRSsubjects Time 1 2 3 4 5 6 ANKRD22_Affy 7.953749641 12.128936177.152671629 11.15686619 T-12 ANXA3_Affy 8.694223626 12.600703587.888396707 11.77082747 T-12 BCL2A1_Affy 7.44669819 11.984971816.831314584 10.15766376 T-12 CCL5_Affy 8.898803629 12.8447038410.18676932 12.76079187 T-12 CD86_Affy 7.053070944 9.2799676037.868508261 9.428684419 T-12 CEACAM1_Affy 7.392836443 10.114299866.897178024 8.990696622 T-12 CRTAP_Affy 7.96569575 10.047920448.402744052 10.11557268 T-12 CSF1R_Affy 8.12973354 9.8020402068.308888545 10.10429638 T-12 FAD104_Affy 7.670795141 10.632469615.857923276 9.290926038 T-12 FCGR1A_Affy 6.796282224 9.517197976.335911956 9.135545399 T-12 GADD45A_Affy 7.874842459 10.634264096.244572007 9.859106279 T-12 GADD45B_Affy 8.479965651 10.290018837.992247915 9.49903678 T-12 HLA.DRA_Affy 9.174921757 12.4016443710.91664573 12.59721891 T-12 IFNGR1_Affy 9.881934243 12.2035266410.20622485 12.04433802 T-12 IL18R1_Affy 5.617961135 10.585993065.401354816 8.680200674 T-12 INSL3_Affy 6.498476621 8.4531021016.380834209 7.658960107 T-12 IRAK2_Affy 6.875497116 9.681308446.749722992 8.274075293 T-12 IRAK4_Affy 7.433778424 8.8785667277.144224029 8.232236692 T-12 ITGAM_Affy 10.49945783 12.4053732910.40124379 12.02696288 T-12 JAK2_Affy 5.855248527 8.7992773785.832795685 7.719583127 T-12 LDLR_Affy 6.257275326 9.6742926146.131286337 7.912581783 T-12 LY96_Affy 8.415332968 11.705704878.133946247 10.62910735 T-12 MAP2K6_Affy 7.596722579 10.566522747.340508485 9.871737402 T-12 MAPK14_Affy 8.070466208 10.921648267.114917424 10.02925699 T-12 MKNK1_Affy 9.279266935 11.720518918.987912062 11.32996284 T-12 Gene_MMP9_Affy 11.53180146 14.2311907710.53501559 13.54148736 T-12 NCR1_Affy 5.317900783 6.5182342785.112152822 6.108537679 T-12 OSM_Affy 6.711016689 8.50414686 5.9145444067.870308905 T-12 PFKFB3_Affy 8.872516729 12.35520872 8.11905201411.65471067 T-12 PRV1_Affy 8.247231438 14.06000214 7.78742063912.82213193 T-12 PSTPIP2_Affy 7.917121337 10.93727651 7.63399916110.56933315 T-12 SOCS3_Affy 6.800978821 10.18734767 6.0955479258.999643638 T-12 SOD2_Affy 9.638778614 11.7873112 8.47727019511.33496413 T-12 TDRD9_Affy 5.982800228 10.55761021 5.3940355799.208195372 T-12 TGFBI_Affy 7.954609344 11.19226116 8.97384830811.1560402 T-12 TIFA_Affy 5.620694754 8.03295421 5.430693745 7.140866404T-12 TNFSF10_Affy 9.847323138 11.63713214 9.035999538 11.33035464 T-12TNFSF13B_Affy 8.585414556 11.79818987 8.90534601 11.34212616 T-12IL10alpha_Affy 8.690880784 10.54878771 9.024669305 10.52469989 T-12ANKRD22.18S 14.554 20.607 18.026 22.621 T-12 ANXA3.18S 12.994 18.8914.989 20.069 T-12 ARG2.18S 16.703 22.511 17.729 23.054 T-12 BCL2A1.18S14.219 19.664 15.565 20.206 T-12 CD86.18S 18.391 22.429 18.245 21.426T-12 CEACAM1.18S 15.174 20.37 16.808 21.929 T-12 FCGR1A.18S 12.27317.502 14.289 19.097 T-12 GADD45A.18S 15.697 20.631 18.602 21.118 T-12GADD45B.18S 14.214 17.599 15.52 18.143 T-12 IFNGR1.18S 13.746 17.41814.233 17.389 T-12 IL1RN.18S 13.7 18.656 14.995 19.231 T-12 IL18R1.18S15.6 21.649 16.63 23.144 T-12 INSL3.18S 18.077 22.515 19.735 23.106 T-12IRAK2.18S 16.851 21.205 17.775 22.286 T-12 IRAK4.18S 16.637 19.16417.001 19.648 T-12 ITGAM.18S 12.6 17.346 13.709 17.172 T-12 JAK2.18S15.292 19.228 16.431 19.001 T-12 LDLR.18S 16.909 21.4 18.195 22.081 T-12LY96.18S 16.811 20.726 17.543 20.956 T-12 MAP2K6.18S 14.062 18.94216.2005 20.678 T-12 MKNK1.18S 14.677 18.261 15.589 19.195 T-12Gene_MMP9.18S 10.128 16.281 12.644 17.265 T-12 NCR1.18S 18.175 23.46219.544 23.756 T-12 OSM.18S 16.6 20.544 17.835 22.174 T-12 PFKFB3.18S12.897 18.994 14.446 20.982 T-12 PRV1.18S 9.4745 19.6 11.749 23.823 T-12PSTPIP2.18S 14.243 18.013 16.098 18.792 T-12 SOCS3.18S 13.312 18.09414.603 18.51 T-12 SOD2.18S 10.822 14.875 11.836 15.269 T-12 TDRD9.18S17.069 23.9 20.012 25.404 T-12 TIFA.18S 14.66 20.607 16.122 20.522 T-12TLR4.18S 15.013 19.366 15.452 19.347 T-12 TNFRSF6.18S 14.771 18.4216.046 18.633 T-12 TNFSF10.18S 14.175 16.71 14.913 18.005 T-12TNFSF13B.18S 13.296 17.2755 14.113 18.23 T-12 VNN1.18S 12.363 19.69514.788 20.209 T-12 AlphaFetoprotein_RBM 0.465 28.9 0.862 15 T-12ApolipoproteinCIII_RBM 9.1 104 14 170 T-12 Beta2Microglobulin_RBM 0.91217 0.815 14.2 T-12 CReactiveProtein_RBM 5.2 743 9.3 435 T-12 IL6_RBM11.9 350000 5.85 1090 T-12 IL8_RBM 5.7 2430 4.6 136 T-12 IL10_RBM 9.41080 6.73 115 T-12 MCP1_RBM 68 20100 54 1860 T-12 Protein_MMP9_RBM 82.76100 37 5500 T-12 TIMP1_RBM 99.6 1670 91.7 777 T-12 ARG2_SPM 18.19 22.8819.11 23.53 T-12 CD86_SPM 16.51 20.02 15.83 18.57 T-12 FCGR1A_SPM 11.7816.69 13.448 18.43 T-12 IL1RN_SPM 12.82 17.055 12.92 17.13 T-12 IL6_SPM22.71 24.493 22.61 24.22 T-12 IL8_SPM 17.728 23.645 19.218 24.27 T-12IL10_SPM 16.905 22.09 17.41 23.38 T-12 IL18R1_SPM 13.69 19.36 13.62820.7 T-12 ITGAM_SPM 12.33 16.26 12.21 16.24 T-12 Gene_MMP9_SPM 8.5314.62 9.58 15.92 T-12 TIMP1_SPM 11.63 15.048 11.71 14.93 T-12 TLR4_SPM12.5 16.64 13.61 16.06 T-12 TNFSF13B_SPM 12.29 15.86 12.37 15.998 T-12CReactiveProtein_SPM 16.23 24.313 18.803 24.245 T-12 IL6_BDB−0.158501071 12.45941145 −0.166932601 −0.076083727 T-12 IL8_BDB−0.260734671 10.31273265 −0.284430649 −0.128681359 T-12 IL10_BDB−0.317504403 4.487381808 −0.541755786 0.071431589 T-12 MCP1_BDB−0.215354271 11.61107183 −0.231122309 −0.10084064 T-12 IL6_CBA−0.674647421 5.170323126 −0.825867097 −0.022530263 T-12 IL8_CBA−0.620553789 5.923519248 −0.821374574 2.902006709 T-12 IL10_CBA−0.661588916 4.74196577 −0.730146126 6.047793474 T-12 ANKRD22_Affy7.396771628 11.57062629 7.129021339 9.443944368 T-36 ANXA3_Affy8.70013622 12.54023119 8.331913923 12.10925762 T-36 BCL2A1_Affy7.183882918 11.76715511 6.405590222 10.37097944 T-36 CCL5_Affy9.328902305 12.61675161 10.40637568 12.86027708 T-36 CD86_Affy6.732706362 9.538835708 7.716006192 9.498278669 T-36 CEACAM1_Affy7.13157116 9.873881135 7.032079139 9.080997559 T-36 CRTAP_Affy7.558590076 9.700256103 8.59265066 10.03618173 T-36 CSF1R_Affy7.837660302 9.949036275 8.464494614 10.30550413 T-36 FAD104_Affy7.014972123 10.15391937 6.239710352 8.845757597 T-36 FCGR1A_Affy6.911908197 9.049485977 6.738241369 8.397213121 T-36 GADD45A_Affy7.56337487 10.85396197 6.589642416 9.749741171 T-36 GADD45B_Affy8.51689696 10.06660743 8.254872004 9.532337644 T-36 HLA.DRA_Affy9.633249373 12.27644605 10.46875169 12.67263889 T-36 IFNGR1_Affy10.57959732 12.27228887 9.808984607 11.81736908 T-36 IL18R1_Affy5.49992437 10.36284472 5.348365647 9.056683263 T-36 INSL3_Affy6.751235769 8.470974618 6.354223568 7.671828775 T-36 IRAK2_Affy6.667783945 8.746281062 6.803703241 7.823003239 T-36 IRAK4_Affy7.263029682 8.81320843 7.431560803 8.530795242 T-36 ITGAM_Affy10.78220173 12.65177246 10.81608481 11.95316198 T-36 JAK2_Affy6.39933742 8.609398755 6.113876348 7.761009287 T-36 LDLR_Affy6.592115082 9.576882573 6.429539125 7.875471828 T-36 LY96_Affy8.653718448 11.10873698 7.823465941 10.99470657 T-36 MAP2K6_Affy7.76590836 10.96462704 7.656234297 9.824692325 T-36 MAPK14_Affy8.115337243 10.65189637 8.035017587 10.2553217 T-36 MKNK1_Affy9.648189763 11.7078247 9.01371136 10.87910413 T-36 Gene_MMP9_Affy11.11827617 14.10554558 11.20801397 13.49402871 T-36 NCR1_Affy5.193987983 6.909045684 5.005834897 6.207479976 T-36 OSM_Affy6.580322029 7.935265514 6.196973735 7.452713795 T-36 PFKFB3_Affy9.424977102 12.34048574 8.499191685 11.18985748 T-36 PRV1_Affy8.075175165 13.89885359 7.885024966 13.11270704 T-36 PSTPIP2_Affy8.014852906 10.87208092 7.546671853 9.463070109 T-36 SOCS3_Affy7.377913606 9.55384035 6.2535144 8.311473326 T-36 SOD2_Affy 9.24608814811.68031375 9.036280605 11.01924042 T-36 TDRD9_Affy 6.24825245411.18511333 5.639880173 9.344436192 T-36 TGFBI_Affy 7.99469534211.02064264 9.079527674 11.18636937 T-36 TIFA_Affy 5.5219785317.303421302 5.384514217 6.593248984 T-36 TNFSF10_Affy 9.6019787211.18227009 9.614398352 11.21902779 T-36 TNFSF13B_Affy 9.37657238811.47004606 8.423154647 11.14896209 T-36 IL10alpha_Affy 8.83818093110.20810676 9.056040579 10.61188847 T-36 ANKRD22.18S 16.059 21.63518.344 22.188 T-36 ANXA3.18S 13.508 18.565 14.637 19.196 T-36 ARG2.18S16.7215 22.085 16.452 23.1025 T-36 BCL2A1.18S 14.892 20.169 15.55821.208 T-36 CD86.18S 18.109 22.671 17.734 21.408 T-36 CEACAM1.18S 15.86420.381 17.141 21.711 T-36 FCGR1A.18S 12.919 17.238 13.773 18.616 T-36GADD45A.18S 16.021 20.468 17.567 21.221 T-36 GADD45B.18S 14.051 16.79915.43 18.189 T-36 IFNGR1.18S 13.89 16.44 14.532 17.1035 T-36 IL1RN.18S14.2965 18.584 14.569 18.818 T-36 IL18R1.18S 15.6475 21.683 17.06821.9465 T-36 INSL3.18S 18.708 21.952 19.775 23.714 T-36 IRAK2.18S 17.56320.591 18.878 21.765 T-36 IRAK4.18S 16.688 18.592 16.633 19.467 T-36ITGAM.18S 12.974 16.862 12.696 16.597 T-36 JAK2.18S 15.659 18.42 16.18518.67 T-36 LDLR.18S 16.925 20.765 18.764 21.351 T-36 LY96.18S 17.01820.384 17.293 21.37 T-36 MAP2K6.18S 14.375 19.44 15.5485 20.507 T-36MKNK1.18S 15.064 17.922 15.281 19.103 T-36 Gene_MMP9.18S 10.4315 16.07512.673 16.9025 T-36 NCR1.18S 18.189 22.531 20.01 23.281 T-36 OSM.18S16.79 20.311 18.219 21.612 T-36 PFKFB3.18S 13.095 18.678 14.042 19.647T-36 PRV1.18S 9.732 18.921 10.63 20.57 T-36 PSTPIP2.18S 14.568 17.94315.363 18.848 T-36 SOCS3.18S 13.309 16.962 14.629 18.503 T-36 SOD2.18S11.004 15.117 12.246 14.872 T-36 TDRD9.18S 17.108 22.863 19.203 24.013T-36 TIFA.18S 15.498 20.069 16.808 20.769 T-36 TLR4.18S 15.77 19.35215.578 19.604 T-36 TNFRSF6.18S 14.782 17.867 15.952 18.014 T-36TNFSF10.18S 14.049 16.332 14.388 17.196 T-36 TNFSF13B.18S 13.898 16.89613.276 18.038 T-36 VNN1.18S 12.406 18.076 13.061 20.4895 T-36AlphaFetoprotein_RBM 0.0878 45.2 0.399 14 T-36 ApolipoproteinCIII_RBM8.1 105 17 143 T-36 Beta2Microglobulin_RBM 0.857 13.4 0.953 10.6 T-36CReactiveProtein_RBM 9.3 548 45 735 T-36 IL6_RBM 8.93 6140 5.85 1480T-36 IL8_RBM 2.4 977 2.5 429 T-36 IL10_RBM 2.68 397 4.4 760 T-36MCP1_RBM 77 4280 60 2170 T-36 Protein_MMP9_RBM 59 9000 73 3840 T-36TIMP1_RBM 89.9 1130 85.2 1050 T-36 ARG2_SPM 17.868 23.15 17.68 23.42T-36 CD86_SPM 16.15 20.528 16.36 18.78 T-36 FCGR1A_SPM 12.53 17.4313.633 17.868 T-36 IL1RN_SPM 13.375 17.043 13.98 17.695 T-36 IL6_SPM23.12 24.378 23.17 24.46 T-36 IL8_SPM 20.33 23.78 19.38 24.24 T-36IL10_SPM 16.785 23.34 18.648 23.63 T-36 IL18R1_SPM 14.16 19.13 15.9819.68 T-36 ITGAM_SPM 12.55 15.84 12.683 15.79 T-36 Gene_MMP9_SPM 9.16315 10.66 14.91 T-36 TIMP1_SPM 11.53 14.895 11.77 15.08 T-36 TLR4_SPM13.2 15.91 13.52 16 T-36 TNFSF13B_SPM 12.56 15.415 13.19 16.725 T-36CReactiveProtein_SPM 19.6 23.97 20.8 24 T-36 IL6_BDB −0.6844654642.880494108 −0.730942369 1.994658691 T-36 IL8_BDB −0.8258365213.74397758 −1.025991342 0.789180774 T-36 IL10_BDB −1.3710582528.324182809 −1.079489666 2.72772331 T-36 MCP1_BDB −0.801635322.625526703 −0.828696306 2.820125749 T-36

7. REFERENCES CITED

The present invention can be implemented as a computer program productthat comprises a computer program mechanism embedded in a computerreadable storage medium. For instance, the computer program productcould contain the program modules shown in FIG. 35. These programmodules can be stored on a CD-ROM, DVD, magnetic disk storage product,or any other computer readable data or program storage product. Theprogram modules can also be embedded in permanent storage, such as ROM,one or more programmable chip, or one or more application specificintegrated circuits (ASICs). Such permanent storage can be localized ina server, 802.11 access point, 802.11 wireless bridge/station, repeater,router, mobile phone, or other electronic devices. The software modulesin the computer program product can also be distributed electronically,via the Internet or otherwise, by transmission of a computer data signal(in which the software modules are embedded) either digitally or on acarrier wave.

Having now fully described the invention with reference to certainrepresentative embodiments and details, it will be apparent to one ofordinary skill in the art that changes and modifications can be madethereto without departing from the spirit or scope of the invention asset forth herein. The specific embodiments described herein are offeredby way of example only, and the invention is to be limited only by theterms of the appended claims, along with the full scope of equivalentsto which such claims are entitled.

1-74. (canceled)
 75. A micro array comprising a plurality of probespots, wherein at least twenty percent of the probe spots in theplurality of probe spots correspond to a plurality of biomarkers listedin Table I, wherein the plurality of biomarkers comprises at least sixbiomarkers listed in Table I when the plurality of biomarkers comprisesboth IL-6 and IL-8.
 76. The micro array of claim 75, wherein said microarray comprises one or more control spots.
 77. The micro array of claim75, wherein at least forty percent of the probe spots in the pluralityof probe spots correspond to biomarkers listed in Table I.
 78. The microarray of claim 75, wherein the micro array consists of between aboutthree and fifty probe spots on a substrate.
 79. The micro array of claim75, wherein said micro array is a nucleic acid micro array.
 80. Themicro array of claim 75, wherein said micro array is a proteinmicroarray.
 81. A kit for predicting the development of sepsis in a testsubject, the kit comprising a plurality of antibodies that,collectively, specifically bind at least three biomarkers listed inTable
 1. 82. A kit for predicting the development of sepsis in a testsubject, the kit comprising a plurality of antibodies that,collectively, specifically bind at least three biomarkers listed inTable K.
 83. A computer program product, wherein the computer programproduct comprises a computer readable storage medium and a computerprogram mechanism embedded therein, the computer program mechanismcomprising: instructions for evaluating whether a plurality of featuresin a biomarker profile of a test subject at risk for developing sepsissatisfies a first value set, wherein satisfying the first value setpredicts that the test subject is likely to develop sepsis, and whereinthe plurality of features are measurable aspects of a plurality ofbiomarkers, the plurality of biomarkers comprising at least threebiomarkers listed in Table I, wherein the plurality of biomarkerscomprises at least six biomarkers listed in Table I when the pluralityof biomarkers comprises both IL-6 and IL-8.
 84. The computer programproduct of claim 83, the computer program product further comprising:instructions for evaluating whether the plurality of features in thebiomarker profile of the test subject satisfies a second value set,wherein satisfying the second value set predicts that the test subjectis not likely to develop sepsis.
 85. The computer program product ofclaim 83, wherein said biomarker profile consists of between 3 and 50biomarkers listed in Table
 1. 86. The computer program product of claim83, wherein said biomarker profile consists of between 3 and 40biomarkers listed in Table
 1. 87. The computer program product of claim83, wherein the plurality of biomarkers comprises at least fourbiomarkers listed in Table
 1. 88. The computer program product of claim83, wherein the plurality of biomarkers comprises at least eightbiomarkers listed in Table I.
 89. A computer comprising: a centralprocessing unit; a memory coupled to the central processing unit, thememory storing: instructions for evaluating whether a plurality offeatures in a biomarker profile of a test subject at risk for developingsepsis satisfies a first value set, wherein satisfying the first valueset predicts that the test subject is likely to develop sepsis, andwherein the plurality of features are measurable aspects of a pluralityof biomarkers, the plurality of biomarkers comprising at least threebiomarkers from Table I, wherein the plurality of biomarkers comprisesat least six biomarkers listed in Table I when the plurality ofbiomarkers comprises both IL-6 and IL-8.
 90. The computer of claim 89,the memory further storing: instructions for evaluating whether theplurality of features in the biomarker profile of the test subjectsatisfies a second value set, wherein satisfying the second value setpredicts that the test subject is not likely to develop sepsis.
 91. Thecomputer of claim 89, wherein said biomarker profile consists of between3 and 50 biomarkers listed in Table
 1. 92. The computer of claim 89,wherein said biomarker profile consists of between 3 and 40 biomarkerslisted in Table
 1. 93. The computer of claim 89, wherein the pluralityof biomarkers comprises at least four biomarkers listed in Table I. 94.The computer of claim 89, wherein the plurality of biomarkers comprisesat least eight biomarkers listed in Table I.
 95. A computer system fordetermining whether a subject is likely to develop sepsis, the computersystem comprising: a central processing unit; and a memory, coupled tothe central processing unit, the memory storing: instructions forobtaining a biomarker profile of a test subject, wherein said biomarkerprofile comprises a plurality of features and wherein the plurality offeatures are measurable aspects of a plurality of biomarkers, theplurality of biomarkers comprising at least three biomarkers listed inTable I; instructions for transmitting the biomarker profile to a remotecomputer, wherein the remote computer includes instructions forevaluating whether the plurality of features in the biomarker profile ofthe test subject satisfies a first value set, wherein satisfying thefirst value set predicts that the test subject is likely to developsepsis; and instructions for receiving a determination, from the remotecomputer, as to whether the plurality of features in the biomarkerprofile of the test subject satisfies the first value set; andinstructions for reporting whether the plurality of features in thebiomarker profile of the test subject satisfies the first value set,wherein the plurality of biomarkers comprises at least six biomarkerslisted in Table I when the plurality of biomarkers comprises both 1L-6and 1L-8. 96-113. (canceled)